WO2015140947A1 - Information processing device, information processing method, and program - Google Patents

Information processing device, information processing method, and program Download PDF

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Publication number
WO2015140947A1
WO2015140947A1 PCT/JP2014/057447 JP2014057447W WO2015140947A1 WO 2015140947 A1 WO2015140947 A1 WO 2015140947A1 JP 2014057447 W JP2014057447 W JP 2014057447W WO 2015140947 A1 WO2015140947 A1 WO 2015140947A1
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WO
WIPO (PCT)
Prior art keywords
user
sales
similarity
purchasing
information
Prior art date
Application number
PCT/JP2014/057447
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French (fr)
Japanese (ja)
Inventor
藤田　貴史
ユワラジュ シンデ
Original Assignee
楽天株式会社
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Filing date
Publication date
Application filed by 楽天株式会社 filed Critical 楽天株式会社
Priority to JP2014532153A priority Critical patent/JP5661977B1/en
Priority to US15/126,851 priority patent/US10546334B2/en
Priority to PCT/JP2014/057447 priority patent/WO2015140947A1/en
Publication of WO2015140947A1 publication Critical patent/WO2015140947A1/en

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0609—Buyer or seller confidence or verification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0613—Third-party assisted
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0621—Item configuration or customization
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0631—Item recommendations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0641—Shopping interfaces
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/08—Auctions

Definitions

  • the present invention relates to an information processing apparatus capable of providing a commodity sales service such as an auction site on the Internet, and an information processing method and program in the information processing apparatus.
  • a user on the purchaser side sometimes registers a user on the seller side as a favorite.
  • a bidder in an auction system registers an exhibitor who has made a successful bid and an exhibitor who has exhibited a product that matches his / her preference.
  • Patent Document 1 describes that the product or service to be evaluated with the highest score and the recommendation of another user who shares the product or service with the lowest score with the user.
  • Patent Document 1 a user is recommended under a very limited condition that the evaluation target with the highest score and the evaluation target with the lowest score are in common. Other users who are very close will not be recommended to users unless they share a disliked product or service. That is, according to the technology, there is a case where a user's opportunity to purchase a product or service is lost because other users whose preferences match the user are not recommended.
  • the server in selecting another user recommended for a certain user, the server has an evaluation target with the highest score given by the other user and an evaluation target with the lowest score. There is also a problem that the processing load on the server is heavy.
  • all other users recommended for the user are users on the same buyer side as the user, for example, a user on the seller side and a buyer such as an online auction. In the system where there are two users, matching between the two is not supported.
  • an object of the present invention is to provide an information processing apparatus and information capable of recommending a seller who is likely to be interested in a purchaser while reducing a processing load in a commodity trading system. It is to provide a processing method and a program.
  • an information processing apparatus includes a storage unit, a communication unit, and a control unit.
  • the storage unit includes favorite information indicating a relationship between a sales user on a sales website of a product or service and a purchase user who has registered the sales user as a favorite, the product or service sold to the sales user, and the purchase.
  • the similarity information regarding the similarity with the goods or service purchased by the user is stored.
  • the communication unit can communicate with the terminal of the sales user and the terminal of the purchase user.
  • the control unit receives action information indicating a predetermined action for the first selling user from the first purchasing user via the trading website from the terminal of the first purchasing user by the communication unit.
  • the control unit calculates the similarity between the first sales user and the second sales user or the similarity between the first purchase user and the second purchase user based on the similarity information. Is possible. Furthermore, the control unit has a degree of similarity greater than or equal to a predetermined value with the first sales user among the extracted second sales users, or is greater than or equal to a predetermined value with the first purchase user. It is possible to extract a third sales user registered as a favorite by a second purchasing user having a similarity. And a control part can control the said communication part so that the web page containing the recommendation information which recommends the said 3rd sales user may be transmitted to a terminal of a said 1st purchase user.
  • the information processing apparatus filters the second sales user favorite registered by the second purchasing user who has registered the first sales user as a favorite, thereby reducing the number of recommended candidates. While reducing the processing load, it is possible to recommend a sales user who is likely to be interested in the purchase user.
  • the control unit displays a web page including a list of display fields of the recommended information that can be displayed by the terminal of the first purchasing user, and the similarity between the first selling user and the second selling user.
  • the higher the degree of similarity between the first purchase user and the second purchase user the higher priority is given to the display column of the recommendation information of the third sales user recommended based on the similarity. It may be generated to be displayed automatically.
  • the information processing apparatus can activate the subsequent purchase user's behavior by preferentially recommending the sales user who is likely to be interested in the purchase user.
  • “preferentially displayed” means that, for example, the area of the display column of recommendation information is set large, or the position thereof is set at the top, but is not limited to these examples.
  • the display column for the recommendation information may include an image showing a product sold by the third sales user.
  • the control unit increases the similarity between the first selling user and the second selling user or the similarity between the first purchasing user and the second purchasing user.
  • the web page may be generated so that the number of images indicating the products of the third sales user recommended based on the similarity is increased.
  • the information processing apparatus can activate the purchasing activity of the product by making the product of the sales user more likely to be interested in the purchase user easier to touch the purchase user.
  • the display column for the recommendation information may include an image showing a product sold by the third sales user.
  • the control unit may generate the web page so that the image includes an image of a product other than the product purchased by the first purchase user.
  • the information processing apparatus can increase the possibility of purchasing the product by the purchasing user by excluding the image of the product already purchased by the purchasing user from the display target of the web page.
  • the control unit extracts a plurality of extracted similarities between the first selling user and the second selling user or similarities between the first purchasing user and the second purchasing user. If the third sales user is the same, the number of purchase users who have registered the plurality of third sales users as favorites respectively is compared, and the third sales user registered to more purchasing users.
  • the web page may be generated so that the display column of the recommended information is preferentially displayed in the list.
  • the information processing apparatus can preferentially recommend purchasing users supported by more purchasing users.
  • An information processing method includes: Favorite information indicating the relationship between a sales user on a product or service trading website and a purchasing user who has registered the selling user as a favorite, and a product or service sold to the selling user and purchased by the purchasing user Storing similarity information about similarity to goods or services;
  • action information indicating a predetermined action for the first selling user from the first purchasing user via the trading website is received from the terminal of the first purchasing user, based on the favorite information, Extracting a second sales user in which a second purchasing user who has registered the first sales user as a favorite has registered as a favorite; Calculating the similarity between the first sales user and the second sales user or the similarity between the first purchase user and the second purchase user based on the similarity information;
  • the first sales user has a similarity greater than or equal to a predetermined value, or has a similarity greater than or equal to a predetermined value with the first purchase user. Extracting a third sales user registered as a favorite by
  • a program according to another embodiment of the present invention is provided in an information processing device.
  • Favorite information indicating the relationship between a sales user on a product or service trading website and a purchasing user who has registered the selling user as a favorite, and a product or service sold to the selling user and purchased by the purchasing user Storing similarity information relating to the similarity to the product or service;
  • action information indicating a predetermined action for the first selling user from the first purchasing user via the trading website is received from the terminal of the first purchasing user, based on the favorite information, Extracting a second sales user favorite registered by a second purchasing user who has registered the first sales user as a favorite; Calculating the similarity between the first sales user and the second sales user or the similarity between the first purchase user and the second purchase user based on the similarity information;
  • the first sales user has a similarity greater than or equal to a predetermined value, or has a similarity greater than or equal to a predetermined value with the first purchase user.
  • FIG. 1 It is a figure showing composition of an auction system concerning an embodiment of the present invention. It is the figure which showed the hardware constitutions of the auction server in the said system. It is the figure which showed the structure of the database which the said auction server has. It is the flowchart which showed the flow of the exhibitor recommendation process by the said auction server. It is the schematic diagram which showed the relationship between the said exhibitor recommendation process and each user. It is the figure which showed the example of the web page containing the recommendation information produced
  • FIG. 1 is a diagram showing a configuration of an auction system according to the present embodiment.
  • this system includes an auction server 100 on the Internet 50 and a plurality of user terminals 200.
  • the auction server 100 is a server managed by the operator of the auction site, and is connected to the user terminal 200 via the Internet 50.
  • the user terminal 200 is a terminal used by users who can participate in the auction, and is, for example, a smartphone, a mobile phone, a tablet PC (Personal Computer), a notebook PC, a desktop PC, or the like.
  • the auction server 100 In response to an access request from the user terminal 200, the auction server 100 generates a web page (HTML (HyperText ⁇ Markup Language) document) having various information items related to the items for sale in the auction, and sends it to the user terminal 200. Send back.
  • HTML HyperText ⁇ Markup Language
  • the user terminal 200 accesses the auction server 100, receives the web page, and displays it on the screen by a browser.
  • the user of the user terminal 200 can place an item for sale in the auction as an exhibitor through the screen displayed by the browser, and can also bid for the item for auction as a bidder.
  • the user can be an exhibitor or a bidder.
  • the user tends to be biased to one of the actions of listing and bidding.
  • users who tend to exhibit are sometimes referred to as exhibitors (exhibiting users), and users who tend to tender are sometimes referred to as bidders (bid users).
  • a bidder selects a favorite seller such as an exhibitor who has exhibited his / her favorite product as a favorite via the user terminal 200 based on past bidding or successful bid experience. Registration with the auction server 100 is possible.
  • the auction server 100 can also recommend other sellers that the bidder may be interested in. .
  • FIG. 2 is a diagram showing a hardware configuration of the auction server 100.
  • an auction server 100 includes a CPU (Central Processing Unit) 11, a ROM (Read Only Memory) 12, a RAM (Random Access Memory) 13, an input / output interface 15, and a bus 14 for connecting them together. Is provided.
  • CPU Central Processing Unit
  • ROM Read Only Memory
  • RAM Random Access Memory
  • the CPU 11 appropriately accesses the RAM 13 or the like as necessary, and comprehensively controls each block of the auction server 100 while performing various arithmetic processes.
  • the ROM 12 is a non-volatile memory in which an OS to be executed by the CPU 11, firmware such as programs and various parameters are fixedly stored.
  • the RAM 13 is used as a work area for the CPU 11 and temporarily holds the OS, various applications being executed, and various data being processed.
  • a display unit 16 an operation receiving unit 17, a storage unit 18, a communication unit 19 and the like are connected to the input / output interface 15.
  • the display unit 16 is a display device using, for example, an LCD (Liquid Crystal Display), an OELD (Organic ElectroLuminescence Display), a CRT (Cathode Ray Tube), or the like.
  • LCD Liquid Crystal Display
  • OELD Organic ElectroLuminescence Display
  • CRT Cathode Ray Tube
  • the operation receiving unit 17 is, for example, a pointing device such as a mouse, a keyboard, a touch panel, and other input devices.
  • the operation reception unit 17 is a touch panel
  • the touch panel can be integrated with the display unit 16.
  • the storage unit 18 is, for example, a nonvolatile memory such as an HDD (Hard Disk Drive), a flash memory (SSD; Solid State Drive), or other solid-state memory.
  • the storage unit 18 stores the OS, various applications, and various data.
  • the storage unit 18 includes an exhibition information database, a bid information database, a favorite registration information database, and a similarity information database.
  • the communication unit 19 is, for example, an NIC (Network Interface Card) for Ethernet, and performs communication processing with the user terminal 200.
  • NIC Network Interface Card
  • FIG. 4 is a diagram showing the configuration of the database that the auction server 100 has.
  • the auction server 100 has an exhibition information database 31, a bid information database 32, a favorite registration information database 33, and a similarity information database 34 in the storage unit 18.
  • the exhibition information database 31 includes exhibitor information such as an exhibitor's user name and contact information, an auction ID of an auction held by the exhibitor, an item ID of an item exhibited in the auction, an item name, an item description text, an item Information such as images and manufacturer names is stored.
  • the bid information database 32 includes bidder information such as the bidder's user name and contact information, auction ID of the auction in which the bidder participated in the bid, product ID of the bid or successful bid, product name, manufacturer name, etc. Is remembered.
  • the favorite registration information database 33 includes information for each seller of a bidder who has registered one seller as a favorite (including the number of bidders who have registered that seller as a favorite), information on the seller, and bids. It stores information such as information on exhibitors registered as favorites for each seller (including the number of exhibitors registered as favorites by the bidder).
  • a user who has registered the seller as a favorite when viewed from a certain seller may be referred to as a “fan”.
  • the similarity information database 34 stores information indicating the degree of similarity between the product exhibited by the exhibitor and the product bid by the bidder (or sold by the winning bidder).
  • the similarity is calculated from metadata such as a product category, a product name, and a manufacturer name, for example.
  • FIG. 4 is a flowchart showing the flow of exhibitor recommendation processing by the auction server 100.
  • the CPU 11 of the auction server 100 determines whether or not request information indicating an action for the exhibitor has been received from the user terminal 200 (step 41).
  • the user of the user terminal 200 that has transmitted the action request is also referred to as an action user.
  • the action for the exhibitor is, for example, a request for processing for registering the exhibitor as a favorite, but is not limited thereto.
  • the favorite registration request is, for example, on the seller's profile page that can be linked from a specific auction page or auction list page posted on the auction site, and the “register this person as a favorite” button is displayed by the user of the user terminal 200. It is transmitted to the auction server 100 when pressed.
  • the CPU 11 obtains data related to the action target exhibitor from the exhibition information database 31 (step 42).
  • the CPU 11 uses the favorite registration information database 33 to store other listings in which other users (bidders) who have registered the action target exhibitor as favorites are registered in addition to the action target exhibitor. Are extracted as recommendation candidates. In this extraction process, other exhibitors who are already registered as favorites by the action user are excluded.
  • the CPU 11 calculates the similarity between the action user and another user (bidder) who has registered the seller as the action target with reference to the similarity information database 34.
  • the CPU 11 extracts bidders whose similarity with the action user is a predetermined value or more from other bidders who have registered the recommended candidates as favorites (step 45).
  • the CPU 11 extracts, as recommended exhibitors, recommended candidates registered as favorites by the other extracted bidders among the extracted recommended candidates (step 46).
  • the CPU 11 acquires information on the extracted recommended exhibitor from the exhibition information database 31 and prioritizes it according to the degree of similarity used for extracting the recommended exhibitor.
  • An exhibitor list is generated (step 47).
  • the priority according to the degree of similarity is, for example, higher the degree of similarity, the higher the display frame of each recommended seller in the list, or the recommended seller. This means that the list is generated so that many images of the exhibited items are displayed. Further, when the above similarities are the same for different recommended exhibitors, an exhibitor who is registered as a favorite by more bidders (that is, has more fans) is given priority.
  • the CPU 11 generates a web page including the data of the seller as the action target and the generated list of recommended sellers (step 48).
  • the CPU 11 transmits the generated web page to the user terminal 200 as a response page for the user action (step 49).
  • FIG. 5 is a schematic diagram showing the relationship between the recommendation process and each user.
  • the action user A takes action on the user (exhibitor) B
  • the other user (bidder) who has registered the user B as a favorite that is, the user C who is a fan of the user B , D, and E are extracted as other users (exhibitors) F, G, H, I, J, and K.
  • the degree of similarity between the users C, D and E and the action user A is calculated, the users C and D having a degree of similarity greater than or equal to a predetermined value are extracted, and among the recommended candidates, the users C and D are favorite Only registered users F, G and H are recommended to user A as recommended exhibitors.
  • FIG. 6 is a diagram showing an example of a web page including recommendation information generated by the auction server 100 and displayed on the user terminal 200.
  • a favorite registration completion notification area 61 is displayed at the top of the favorite registration completion page, for example.
  • a message to the effect that the seller's favorite registration process for which the user requested the favorite registration has been completed is displayed together with the profile information of the seller.
  • an exhibitor recommendation area 62 is displayed below the favorite registration completion notification area 61.
  • the exhibitor recommendation area 62 a list of recommended exhibitors extracted by the extraction process based on the similarity equal to or higher than the predetermined value shown in FIGS. 5 and 6 is displayed for each display frame.
  • the display frame of each recommended exhibitor In the display frame of each recommended exhibitor, the number of fans of the exhibitor and the evaluation value, a product image 63 of the product that the exhibitor exhibits, and a favorite registration button 64 for registering the exhibitor as a favorite are displayed. Is done.
  • the product image 63 is hyperlinked to a page of a held auction in which the product image 63 is an exhibited product.
  • the area of the display frame of each recommended exhibitor is set larger as the similarity is higher, and is displayed at the top.
  • the user “AAA” is an exhibitor who is registered as a favorite by another bidder who has the highest similarity with the action user.
  • the number of product images 63 displayed in the display frame of each recommended exhibitor is set so that the higher the similarity is, the more the product images 63 are displayed. For example, in the display frame of the user “AAA”, five product images 63 are displayed, but in the display frame of the user “BBB” on the lower left side, only two product images 63 are displayed. Absent. Furthermore, in each display frame of the users “CCC” and “DDD” on the right side, a product image is not displayed, and instead, an exhibition product display button 65 hyperlinked to the product image 63 is displayed.
  • a plurality of product images 63 are displayed, they are randomly extracted. For example, based on the similarity information DB 34, an image of a product with a higher degree of preference regarding the product name, manufacturer, genre, etc. of the action user is extracted. May be.
  • the auction server 100 excludes the product image 63 indicating the product that the action user has already made a successful bid from the extraction target based on the bid information database 32.
  • the auction server 100 determines the action user and the specific seller. Narrow down the number of recommended candidates by filtering other sellers registered as favorites by other bidders based on the similarity between the action user and the other bidders. While reducing the processing load, other exhibitors who are likely to be interested in the action user can be recommended.
  • the similarity between the action user (bidder) and the other users (exhibitors) who have registered the seller to be acted on as favorites is calculated.
  • An exhibitor who is registered as a favorite by another user having a similarity equal to or higher than a predetermined value is presented as a recommended exhibitor.
  • the similarity between the action target exhibitor and the recommended candidate may be calculated, and among the recommended candidates, a recommendation candidate having a similarity greater than or equal to a predetermined value with the action target exhibitor may be presented as the recommended exhibitor. .
  • FIG. 7 is a schematic diagram showing the relationship between the recommendation process and each user in this case.
  • the action user A takes action on the user (exhibitor) B
  • the other user (bidder) who has registered the user B as a favorite that is, the user C who is a fan of the user B
  • Other users (exhibitors) F, G, H, I, J, and K with which, D, and E are registered as favorites are extracted as recommendation candidates.
  • the similarity between the recommendation candidates users F, G, H, I, J, and K and the action target user B is calculated, and among the recommendation candidates, the users F, H that have a similarity greater than or equal to a predetermined value. Only K and K are recommended to the user A as recommended exhibitors.
  • the degree of similarity between users who tend to tend and the degree of similarity between users who tend to exhibit can be used to narrow down recommended exhibitors as the degree of similarity between users who tend to have the same tendency.
  • the area of the display frame of each recommended exhibitor and the number of product images 63 in the exhibitor recommended area 62 may be varied according to the degree of similarity between the seller who is the action target and the recommended candidate.
  • favorite registration has been cited as an action of a user (bidder) for an exhibitor serving as a trigger for recommending other exhibitors.
  • the action is not limited to this.
  • a user's request to view an exhibitor's profile information a request to register a watch list for a certain auction (product) (a list for registering auctions that the user is not bidding but is concerned about), and an evaluation grant request to the seller
  • Other sellers may be recommended on the same web page, with various actions as triggers, along with the response results for those actions.
  • an exhibitor who has not exhibited a product for a predetermined period may be excluded from the recommended candidates.
  • an exhibitor whose evaluation value is equal to or less than a predetermined value may be excluded based on evaluation information from other bidders.
  • the exhibitor registered in the black list generated based on the notification of the auction server from the user terminal of another bidder may be excluded.
  • the area of the display frame of each recommended exhibitor and the number of product images 63 in the exhibitor recommended area 62 are varied according to the similarity between the action user and other users.
  • the number of page views to the profile information page or the listing item list page of the seller to be recommended is a predetermined number or more, the number of the product images 63 is limited, It may not be displayed, and the exhibition product display button 65 hyperlinked to the product image may not be displayed.
  • the present invention can be applied to any trading system that is established by connecting a user who can be a buyer and a user who can be a seller to a server via each user terminal.

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Abstract

When action information indicating a predetermined action from a first purchasing user to a first selling user via a trading website is received by a communication unit from a terminal of the first purchasing user, a second selling user registered as a favorite by a second purchasing user who has registered as a favorite the first selling user is extracted on the basis of favorites information. On the basis of similarity information, similarity between the first selling user and the second selling user or similarity between the first purchasing user and the second purchasing user is calculated, and a third selling user who, among the extracted second selling users, has similarity of a predetermined value or more to the first selling user, or who is registered as a favorite by the second purchasing user having similarity of a predetermined value or more to the first purchasing user is extracted. A control unit controls the communication unit so as to transmit, to the terminal of the first purchasing user, a Web page including recommendation information recommending the third selling user.

Description

情報処理装置、情報処理方法及びプログラムInformation processing apparatus, information processing method, and program
　本発明は、インターネット上でオークションサイト等の商品売買サービスを提供可能な情報処理装置、当該情報処理装置における情報処理方法及びプログラムに関する。 The present invention relates to an information processing apparatus capable of providing a commodity sales service such as an auction site on the Internet, and an information processing method and program in the information processing apparatus.
　オークションシステム等の商品売買システムにおいては、購買者側のユーザが、販売者側のユーザをお気に入り登録することがある。例えば、オークションシステムにおける入札者が、落札した商品の出品者や、自身の嗜好に合致する商品を出品している出品者をお気に入り登録する。 In a merchandise trading system such as an auction system, a user on the purchaser side sometimes registers a user on the seller side as a favorite. For example, a bidder in an auction system registers an exhibitor who has made a successful bid and an exhibitor who has exhibited a product that matches his / her preference.
　また、ユーザが自らお気に入り登録するのみならず、ユーザが気に入るであろう他のユーザをシステムが推薦する技術も存在する。 In addition, there is a technique in which the system recommends other users that the user will like as well as the user's favorite registration.
　下記特許文献１には、最高点を付けた評価対象の商品やサービスと、最低点を付けた商品やサービスがユーザと共通している他のユーザを推薦することが記載されている。 The following Patent Document 1 describes that the product or service to be evaluated with the highest score and the recommendation of another user who shares the product or service with the lowest score with the user.
特開２００９－１２３１９２号公報JP 2009-123192 A
　しかしながら、上記特許文献１に記載の技術では、最高点を付けた評価対象と最低点を付けた評価対象が共通するという極めて限定された条件でユーザが推薦されるため、好きな商品やサービスが極めて近似している他のユーザでも、嫌いな商品やサービスが共通していない限りユーザに推薦されない。すなわち、当該技術ではユーザと嗜好が合致する他のユーザが推薦されないことで、ユーザの商品やサービスの購買の機会が逸失されてしまう場合があった。 However, in the technique described in Patent Document 1, a user is recommended under a very limited condition that the evaluation target with the highest score and the evaluation target with the lowest score are in common. Other users who are very close will not be recommended to users unless they share a disliked product or service. That is, according to the technology, there is a case where a user's opportunity to purchase a product or service is lost because other users whose preferences match the user are not recommended.
　また上記特許文献１に記載の技術では、あるユーザに対して推薦する他のユーザを選定するにあたり、サーバが、他のユーザが最高点を付けた評価対象と、最低点を付けた評価対象とを全て解析する必要があり、サーバの処理負荷が大きいという問題もある。 In addition, in the technique described in Patent Document 1, in selecting another user recommended for a certain user, the server has an evaluation target with the highest score given by the other user and an evaluation target with the lowest score. There is also a problem that the processing load on the server is heavy.
　さらに上記特許文献１に記載の技術では、ユーザに推薦される他のユーザは全て、当該ユーザと同じ購買者側の立場のユーザであり、例えばネットオークションのような販売者側のユーザと購買者のユーザとが存在するシステムにおける両者のマッチングには対応していない。 Further, in the technique described in Patent Document 1, all other users recommended for the user are users on the same buyer side as the user, for example, a user on the seller side and a buyer such as an online auction. In the system where there are two users, matching between the two is not supported.
　以上のような事情に鑑み、本発明の目的は、商品売買システムにおいて、処理負荷を軽減しながら、購買者が関心を持つ可能性の高い販売者を推薦することが可能な情報処理装置、情報処理方法及びプログラムを提供することにある。 In view of the circumstances as described above, an object of the present invention is to provide an information processing apparatus and information capable of recommending a seller who is likely to be interested in a purchaser while reducing a processing load in a commodity trading system. It is to provide a processing method and a program.
　上述の課題を解決するため、本発明の一形態に係る情報処理装置は、記憶部と、通信部と、制御部とを有する。上記記憶部は、商品またはサービスの売買ウェブサイトにおける販売ユーザと当該販売ユーザをお気に入りとして登録している購買ユーザとの関係を示すお気に入り情報と、上記販売ユーザに販売された商品またはサービスと上記購買ユーザによって購買された商品またはサービスとの類似度に関する類似度情報とを記憶する。上記通信部は、上記販売ユーザの端末及び上記購買ユーザの端末と通信可能である。上記制御部は、上記売買ウェブサイトを介した第１の購買ユーザから第１の販売ユーザに対する所定のアクションを示すアクション情報が上記第１の購買ユーザの端末から上記通信部により受信された場合に、上記お気に入り情報を基に、上記第１の販売ユーザをお気に入り登録している第２の購買ユーザがお気に入り登録している第２の販売ユーザを抽出可能である。また制御部は、上記類似度情報を基に、上記第１の販売ユーザと上記第２の販売ユーザとの類似度または上記第１の購買ユーザと上記第２の購買ユーザとの類似度を算出可能である。さらに制御部は、上記抽出された第２の販売ユーザのうち、上記第１の販売ユーザとの間で所定値以上の類似度を有するか、または、上記第１の購買ユーザと所定値以上の類似度を有する第２の購買ユーザがお気に入りとして登録している第３の販売ユーザを抽出可能である。そして制御部は、当該第３の販売ユーザを推薦する推薦情報を含むウェブページを、上記第１の購買ユーザの端末へ送信するように上記通信部を制御可能である。 In order to solve the above-described problem, an information processing apparatus according to an embodiment of the present invention includes a storage unit, a communication unit, and a control unit. The storage unit includes favorite information indicating a relationship between a sales user on a sales website of a product or service and a purchase user who has registered the sales user as a favorite, the product or service sold to the sales user, and the purchase. The similarity information regarding the similarity with the goods or service purchased by the user is stored. The communication unit can communicate with the terminal of the sales user and the terminal of the purchase user. When the control unit receives action information indicating a predetermined action for the first selling user from the first purchasing user via the trading website from the terminal of the first purchasing user by the communication unit. Based on the favorite information, it is possible to extract a second sales user registered as a favorite by a second purchase user who has registered the first sales user as a favorite. Further, the control unit calculates the similarity between the first sales user and the second sales user or the similarity between the first purchase user and the second purchase user based on the similarity information. Is possible. Furthermore, the control unit has a degree of similarity greater than or equal to a predetermined value with the first sales user among the extracted second sales users, or is greater than or equal to a predetermined value with the first purchase user. It is possible to extract a third sales user registered as a favorite by a second purchasing user having a similarity. And a control part can control the said communication part so that the web page containing the recommendation information which recommends the said 3rd sales user may be transmitted to a terminal of a said 1st purchase user.
　これにより情報処理装置は、第１の販売ユーザをお気に入り登録している第２の購買ユーザがお気に入り登録している第２の販売ユーザを、類似度によってフィルタリングすることで、推薦候補の母数を絞って処理負荷を軽減しながら、購買ユーザが関心を持つ可能性の高い販売ユーザを推薦することができる。 In this way, the information processing apparatus filters the second sales user favorite registered by the second purchasing user who has registered the first sales user as a favorite, thereby reducing the number of recommended candidates. While reducing the processing load, it is possible to recommend a sales user who is likely to be interested in the purchase user.
　上記制御部は、上記第１の購買ユーザの端末によって表示可能な上記推薦情報の表示欄の一覧を含むウェブページを、上記第１の販売ユーザと上記第２の販売ユーザとの間の類似度または上記第１の購買ユーザと上記第２の購買ユーザとの間の類似度が高いほど、当該類似度に基づいて推薦される第３の販売ユーザの推薦情報の表示欄が上記一覧内で優先的に表示されるように生成してもよい。 The control unit displays a web page including a list of display fields of the recommended information that can be displayed by the terminal of the first purchasing user, and the similarity between the first selling user and the second selling user. Alternatively, the higher the degree of similarity between the first purchase user and the second purchase user, the higher priority is given to the display column of the recommendation information of the third sales user recommended based on the similarity. It may be generated to be displayed automatically.
　これにより情報処理装置は、購買ユーザが関心を持つ可能性が高い販売ユーザほど優先的に推薦されるようにすることで、その後の購買ユーザの行動を活性化させることができる。ここで「優先的に表示される」とは、例えば推薦情報の表示欄の面積が大きく設定されたり、その位置が上部に設定されたりすることであるが、これらの例に限られない。 Thereby, the information processing apparatus can activate the subsequent purchase user's behavior by preferentially recommending the sales user who is likely to be interested in the purchase user. Here, “preferentially displayed” means that, for example, the area of the display column of recommendation information is set large, or the position thereof is set at the top, but is not limited to these examples.
　上記推薦情報の表示欄は、上記第３の販売ユーザが販売している商品を示す画像を含んでもよい。この場合上記制御部は、上記第１の販売ユーザと上記第２の販売ユーザとの間の類似度または上記第１の購買ユーザと上記第２の購買ユーザとの間の類似度が高いほど、当該類似度に基づいて推薦される第３の販売ユーザの商品を示す画像の数が多くなるように上記ウェブページを生成してもよい。 The display column for the recommendation information may include an image showing a product sold by the third sales user. In this case, the control unit increases the similarity between the first selling user and the second selling user or the similarity between the first purchasing user and the second purchasing user. The web page may be generated so that the number of images indicating the products of the third sales user recommended based on the similarity is increased.
　これにより情報処理装置は、購買ユーザが関心を持つ可能性が高い販売ユーザの商品ほど、購買ユーザの目に触れやすくすることで、当該商品の購買活動を活性化させることができる。 Thereby, the information processing apparatus can activate the purchasing activity of the product by making the product of the sales user more likely to be interested in the purchase user easier to touch the purchase user.
　上記推薦情報の表示欄は、上記第３の販売ユーザが販売している商品を示す画像を含んでもよい。この場合上記制御部は、上記画像として、上記第１の購買ユーザが購買した商品以外の商品の画像が含まれるように上記ウェブページを生成してもよい。 The display column for the recommendation information may include an image showing a product sold by the third sales user. In this case, the control unit may generate the web page so that the image includes an image of a product other than the product purchased by the first purchase user.
　これにより情報処理装置は、購買ユーザが既に購買済みの商品の画像をウェブページの表示対象から除外することで、購買ユーザの商品購買の可能性を高めることができる。 Thereby, the information processing apparatus can increase the possibility of purchasing the product by the purchasing user by excluding the image of the product already purchased by the purchasing user from the display target of the web page.
　上記制御部は、上記第１の販売ユーザと上記第２の販売ユーザとの間の類似度または上記第１の購買ユーザと上記第２の購買ユーザとの間の類似度が、抽出された複数の第３の販売ユーザに関して同一の場合、当該複数の第３の販売ユーザをそれぞれお気に入りとして登録している購買ユーザの数を比較し、より多くの購買ユーザに登録されている第３の販売ユーザの推薦情報の表示欄が上記一覧内で優先的に表示されるように上記ウェブページを生成してもよい。 The control unit extracts a plurality of extracted similarities between the first selling user and the second selling user or similarities between the first purchasing user and the second purchasing user. If the third sales user is the same, the number of purchase users who have registered the plurality of third sales users as favorites respectively is compared, and the third sales user registered to more purchasing users. The web page may be generated so that the display column of the recommended information is preferentially displayed in the list.
　これにより情報処理装置は、より多くの購買ユーザから支持されている購買ユーザを優先的に推薦することができる。 Thereby, the information processing apparatus can preferentially recommend purchasing users supported by more purchasing users.
　本発明の他の形態に係る情報処理方法は、
　商品またはサービスの売買ウェブサイトにおける販売ユーザと当該販売ユーザをお気に入りとして登録している購買ユーザとの関係を示すお気に入り情報と、上記販売ユーザに販売された商品またはサービスと上記購買ユーザによって購買された商品またはサービスとの類似度に関する類似度情報とを記憶すること、
　上記売買ウェブサイトを介した第１の購買ユーザから第１の販売ユーザに対する所定のアクションを示すアクション情報が上記第１の購買ユーザの端末から受信された場合に、上記お気に入り情報を基に、上記第１の販売ユーザをお気に入り登録している第２の購買ユーザがお気に入り登録している第２の販売ユーザを抽出すること、
　上記類似度情報を基に、上記第１の販売ユーザと上記第２の販売ユーザとの類似度または上記第１の購買ユーザと上記第２の購買ユーザとの類似度を算出すること、
　上記抽出された第２の販売ユーザのうち、上記第１の販売ユーザとの間で所定値以上の類似度を有するか、または、上記第１の購買ユーザと所定値以上の類似度を有する第２の購買ユーザがお気に入りとして登録している第３の販売ユーザを抽出すること、及び、
　上記第３の販売ユーザを推薦する推薦情報を含むウェブページを、上記第１の購買ユーザの端末へ送信することを含む。 An information processing method according to another aspect of the present invention includes:
Favorite information indicating the relationship between a sales user on a product or service trading website and a purchasing user who has registered the selling user as a favorite, and a product or service sold to the selling user and purchased by the purchasing user Storing similarity information about similarity to goods or services;
When action information indicating a predetermined action for the first selling user from the first purchasing user via the trading website is received from the terminal of the first purchasing user, based on the favorite information, Extracting a second sales user in which a second purchasing user who has registered the first sales user as a favorite has registered as a favorite;
Calculating the similarity between the first sales user and the second sales user or the similarity between the first purchase user and the second purchase user based on the similarity information;
Among the extracted second sales users, the first sales user has a similarity greater than or equal to a predetermined value, or has a similarity greater than or equal to a predetermined value with the first purchase user. Extracting a third sales user registered as a favorite by two purchasing users; and
Transmitting a web page including recommendation information for recommending the third selling user to the terminal of the first purchasing user.
　本発明のまた別の形態に係るプログラムは、情報処理装置に、
　商品またはサービスの売買ウェブサイトにおける販売ユーザと当該販売ユーザをお気に入りとして登録している購買ユーザとの関係を示すお気に入り情報と、上記販売ユーザに販売された商品またはサービスと上記購買ユーザによって購買された商品またはサービスとの類似度に関する類似度情報とを記憶するステップと、
　上記売買ウェブサイトを介した第１の購買ユーザから第１の販売ユーザに対する所定のアクションを示すアクション情報が上記第１の購買ユーザの端末から受信された場合に、上記お気に入り情報を基に、上記第１の販売ユーザをお気に入り登録している第２の購買ユーザがお気に入り登録している第２の販売ユーザを抽出するステップと、
　上記類似度情報を基に、上記第１の販売ユーザと上記第２の販売ユーザとの類似度または上記第１の購買ユーザと上記第２の購買ユーザとの類似度を算出するステップと、
　上記抽出された第２の販売ユーザのうち、上記第１の販売ユーザとの間で所定値以上の類似度を有するか、または、上記第１の購買ユーザと所定値以上の類似度を有する第２の購買ユーザがお気に入りとして登録している第３の販売ユーザを抽出するステップと、
　上記第３の販売ユーザを推薦する推薦情報を含むウェブページを、上記第１の購買ユーザの端末へ送信するステップと
　を実行させる。 A program according to another embodiment of the present invention is provided in an information processing device.
Favorite information indicating the relationship between a sales user on a product or service trading website and a purchasing user who has registered the selling user as a favorite, and a product or service sold to the selling user and purchased by the purchasing user Storing similarity information relating to the similarity to the product or service;
When action information indicating a predetermined action for the first selling user from the first purchasing user via the trading website is received from the terminal of the first purchasing user, based on the favorite information, Extracting a second sales user favorite registered by a second purchasing user who has registered the first sales user as a favorite;
Calculating the similarity between the first sales user and the second sales user or the similarity between the first purchase user and the second purchase user based on the similarity information;
Among the extracted second sales users, the first sales user has a similarity greater than or equal to a predetermined value, or has a similarity greater than or equal to a predetermined value with the first purchase user. Extracting a third sales user registered as a favorite by two purchasing users;
Transmitting a web page including recommendation information for recommending the third sales user to the terminal of the first purchase user.
　以上のように、本発明によれば、商品売買システムにおいて、処理負荷を軽減しながら、購買ユーザが関心を持つ可能性の高い販売ユーザを推薦することができる。しかし、ここに記載された効果は必ずしも本発明を限定するものではない。 As described above, according to the present invention, it is possible to recommend a sales user who is likely to be interested in a purchasing user while reducing the processing load in the commodity trading system. However, the effects described here do not necessarily limit the present invention.
本発明の実施形態に係るオークションシステムの構成を示した図である。It is a figure showing composition of an auction system concerning an embodiment of the present invention. 上記システムにおけるオークションサーバのハードウェア構成を示した図である。It is the figure which showed the hardware constitutions of the auction server in the said system. 上記オークションサーバが有するデータベースの構成を示した図である。It is the figure which showed the structure of the database which the said auction server has. 上記オークションサーバによる出品者推薦処理の流れを示したフローチャートである。It is the flowchart which showed the flow of the exhibitor recommendation process by the said auction server. 上記出品者推薦処理と各ユーザとの関係を示した模式図である。It is the schematic diagram which showed the relationship between the said exhibitor recommendation process and each user. 上記オークションサーバによって生成されユーザ端末に表示される、推薦情報を含むウェブページの例を示した図である。It is the figure which showed the example of the web page containing the recommendation information produced | generated by the said auction server and displayed on a user terminal. 本発明の他の実施形態における出品者推薦処理と各ユーザとの関係を示した模式図である。It is the schematic diagram which showed the relationship between the exhibitor recommendation process and each user in other embodiment of this invention.
　以下、本発明がオークションシステムに適用された場合の本発明の実施形態を、図面を参照しながら説明する。 Hereinafter, an embodiment of the present invention when the present invention is applied to an auction system will be described with reference to the drawings.
　［システムの構成］
　図１は、本実施形態に係るオークションシステムの構成を示した図である。 [System configuration]
FIG. 1 is a diagram showing a configuration of an auction system according to the present embodiment.
　同図に示すように、このシステムは、インターネット５０上のオークションサーバ１００及び複数のユーザ端末２００を含む。 As shown in the figure, this system includes an auction server 100 on the Internet 50 and a plurality of user terminals 200.
　オークションサーバ１００は、オークションサイトの運営者により管理されるサーバであり、ユーザ端末２００とインターネット５０を介して接続されている。 The auction server 100 is a server managed by the operator of the auction site, and is connected to the user terminal 200 via the Internet 50.
　ユーザ端末２００は、オークションに参加可能なユーザにより使用される端末であり、例えばスマートフォン、携帯電話、タブレットＰＣ（Personal Computer）、ノートブックＰＣ、デスクトップＰＣ等である。 The user terminal 200 is a terminal used by users who can participate in the auction, and is, for example, a smartphone, a mobile phone, a tablet PC (Personal Computer), a notebook PC, a desktop PC, or the like.
　オークションサーバ１００は、ユーザ端末２００からのアクセス要求に応じて、オークションに出品されている商品に関する各種情報項目等を有するウェブページ（ＨＴＭＬ（HyperText Markup Language）文書）を生成して、ユーザ端末２００へ返信する。 In response to an access request from the user terminal 200, the auction server 100 generates a web page (HTML (HyperText 情報 Markup Language) document) having various information items related to the items for sale in the auction, and sends it to the user terminal 200. Send back.
　ユーザ端末２００は、オークションサーバ１００へアクセスし、上記ウェブページを受信して、ブラウザにより画面に表示する。 The user terminal 200 accesses the auction server 100, receives the web page, and displays it on the screen by a browser.
　ユーザ端末２００のユーザは、上記ブラウザによって表示される画面を介して、出品者としてオークションに商品を出品することもできるし、入札者としてオークションの商品に入札することもできる。 The user of the user terminal 200 can place an item for sale in the auction as an exhibitor through the screen displayed by the browser, and can also bid for the item for auction as a bidder.
　すなわち、ユーザは、出品者にも入札者にもなり得る。しかし、多くの場合、ユーザは、出品と入札のうちいずれかの行動に偏る傾向にある。本実施形態においては、出品傾向のユーザを出品者（出品ユーザ）と称し、入札傾向のユーザを入札者（入札ユーザ）と称する場合もある。 That is, the user can be an exhibitor or a bidder. However, in many cases, the user tends to be biased to one of the actions of listing and bidding. In the present embodiment, users who tend to exhibit are sometimes referred to as exhibitors (exhibiting users), and users who tend to tender are sometimes referred to as bidders (bid users).
　また、このオークションシステムでは、入札者は、過去の入札または落札経験等から、例えば自身の好みの商品を出品している出品者等、お気に入りの出品者を、ユーザ端末２００を介して、お気に入りとしてオークションサーバ１００に登録することができる。 Further, in this auction system, a bidder selects a favorite seller such as an exhibitor who has exhibited his / her favorite product as a favorite via the user terminal 200 based on past bidding or successful bid experience. Registration with the auction server 100 is possible.
　また後述するが、オークションサーバ１００は、入札者がある出品者に対するお気に入り登録等の所定のアクションを起こした際に、当該入札者が関心を持つであろう他の出品者を推薦することもできる。 As will be described later, when the bidder takes a predetermined action such as favorite registration for a certain seller, the auction server 100 can also recommend other sellers that the bidder may be interested in. .
［オークションサーバのハードウェア構成］
　図２は、上記オークションサーバ１００のハードウェア構成を示した図である。同図に示すように、オークションサーバ１００は、ＣＰＵ（Central Processing Unit）１１、ＲＯＭ（Read Only Memory）１２、ＲＡＭ（Random Access Memory）１３、入出力インタフェース１５、及び、これらを互いに接続するバス１４を備える。 [Hardware configuration of auction server]
FIG. 2 is a diagram showing a hardware configuration of the auction server 100. As shown in FIG. As shown in the figure, an auction server 100 includes a CPU (Central Processing Unit) 11, a ROM (Read Only Memory) 12, a RAM (Random Access Memory) 13, an input / output interface 15, and a bus 14 for connecting them together. Is provided.
　ＣＰＵ１１は、必要に応じてＲＡＭ１３等に適宜アクセスし、各種演算処理を行いながらオークションサーバ１００の各ブロック全体を統括的に制御する。ＲＯＭ１２は、ＣＰＵ１１に実行させるＯＳ、プログラムや各種パラメータなどのファームウェアが固定的に記憶されている不揮発性のメモリである。ＲＡＭ１３は、ＣＰＵ１１の作業用領域等として用いられ、ＯＳ、実行中の各種アプリケーション、処理中の各種データを一時的に保持する。 The CPU 11 appropriately accesses the RAM 13 or the like as necessary, and comprehensively controls each block of the auction server 100 while performing various arithmetic processes. The ROM 12 is a non-volatile memory in which an OS to be executed by the CPU 11, firmware such as programs and various parameters are fixedly stored. The RAM 13 is used as a work area for the CPU 11 and temporarily holds the OS, various applications being executed, and various data being processed.
　入出力インタフェース１５には、表示部１６、操作受付部１７、記憶部１８、通信部１９等が接続される。 A display unit 16, an operation receiving unit 17, a storage unit 18, a communication unit 19 and the like are connected to the input / output interface 15.
　表示部１６は、例えばＬＣＤ（Liquid Crystal Display）、ＯＥＬＤ（Organic ElectroLuminescence Display）、ＣＲＴ（Cathode Ray Tube）等を用いた表示デバイスである。 The display unit 16 is a display device using, for example, an LCD (Liquid Crystal Display), an OELD (Organic ElectroLuminescence Display), a CRT (Cathode Ray Tube), or the like.
　操作受付部１７は、例えばマウス等のポインティングデバイス、キーボード、タッチパネル、その他の入力装置である。操作受付部１７がタッチパネルである場合、そのタッチパネルは表示部１６と一体となり得る。 The operation receiving unit 17 is, for example, a pointing device such as a mouse, a keyboard, a touch panel, and other input devices. When the operation reception unit 17 is a touch panel, the touch panel can be integrated with the display unit 16.
　記憶部１８は、例えばＨＤＤ（Hard Disk Drive）や、フラッシュメモリ（ＳＳＤ；Solid State Drive）、その他の固体メモリ等の不揮発性メモリである。当該記憶部１８には、上記ＯＳや各種アプリケーション、各種データが記憶される。 The storage unit 18 is, for example, a nonvolatile memory such as an HDD (Hard Disk Drive), a flash memory (SSD; Solid State Drive), or other solid-state memory. The storage unit 18 stores the OS, various applications, and various data.
　後述するが、特に本実施形態において、記憶部１８は、出品情報データベース、入札情報データベース、お気に入り登録情報データベース及び類似度情報データベースを有している。 As will be described later, particularly in the present embodiment, the storage unit 18 includes an exhibition information database, a bid information database, a favorite registration information database, and a similarity information database.
　通信部１９は、例えばEthernet用のＮＩＣ（Network Interface Card）であり、上記ユーザ端末２００との間の通信処理を担う。 The communication unit 19 is, for example, an NIC (Network Interface Card) for Ethernet, and performs communication processing with the user terminal 200.
［オークションサーバのデータベース構成］
　図４は、上記オークションサーバ１００が有するデータベースの構成を示した図である。 [Database configuration of auction server]
FIG. 4 is a diagram showing the configuration of the database that the auction server 100 has.
　同図に示すように、オークションサーバ１００は、記憶部１８に、出品情報データベース３１、入札情報データベース３２、お気に入り登録情報データベース３３及び類似度情報データベース３４を有している。 As shown in the figure, the auction server 100 has an exhibition information database 31, a bid information database 32, a favorite registration information database 33, and a similarity information database 34 in the storage unit 18.
　出品情報データベース３１は、出品者のユーザ名や連絡先等の出品者情報、出品者によって開催されたオークションのオークションＩＤ、当該オークションに出品された商品の商品ＩＤ、商品名、商品説明テキスト、商品画像、メーカー名等の情報を記憶している。 The exhibition information database 31 includes exhibitor information such as an exhibitor's user name and contact information, an auction ID of an auction held by the exhibitor, an item ID of an item exhibited in the auction, an item name, an item description text, an item Information such as images and manufacturer names is stored.
　入札情報データベース３２は、入札者のユーザ名や連絡先等の入札者情報、当該入札者が入札に参加したオークションのオークションＩＤ、入札または落札した商品の商品ＩＤ、商品名、メーカー名等の情報を記憶している。 The bid information database 32 includes bidder information such as the bidder's user name and contact information, auction ID of the auction in which the bidder participated in the bid, product ID of the bid or successful bid, product name, manufacturer name, etc. Is remembered.
　お気に入り登録情報データベース３３は、一の出品者をお気に入り登録している入札者の出品者毎の情報（その出品者をお気に入り登録している入札者の人数も含む）、当該出品者の情報、入札者毎のお気に入り登録している出品者の情報（その入札者がお気に入り登録している出品者の人数も含む）等の情報を記憶している。 The favorite registration information database 33 includes information for each seller of a bidder who has registered one seller as a favorite (including the number of bidders who have registered that seller as a favorite), information on the seller, and bids. It stores information such as information on exhibitors registered as favorites for each seller (including the number of exhibitors registered as favorites by the bidder).
　ここで、本実施形態では、ある出品者から見て、その出品者をお気に入り登録しているユーザを、「ファン」と称する場合もある。 Here, in this embodiment, a user who has registered the seller as a favorite when viewed from a certain seller may be referred to as a “fan”.
　類似度情報データベース３４は、出品者が出品した商品と、入札者が入札した（または落札者が落札した）商品との類似度を示す情報を記憶している。類似度は、例えば、商品のカテゴリ、商品名、メーカー名等のメタデータから算出される。 The similarity information database 34 stores information indicating the degree of similarity between the product exhibited by the exhibitor and the product bid by the bidder (or sold by the winning bidder). The similarity is calculated from metadata such as a product category, a product name, and a manufacturer name, for example.
　これら各データベースは、後述するオークションサーバ１００による入札者への出品者推薦動作の際に用いられる。 These databases are used when an auction server 100 (to be described later) recommends exhibitors to bidders.
［オークションサーバの動作］
　次に、以上のように構成されたオークションサーバ１００の動作について説明する。当該動作は、オークションサーバ１００のＣＰＵ１１及び通信部１９等のハードウェアと、記憶部１８に記憶されたソフトウェアとの協働により実行される。以下の説明では、便宜上、ＣＰＵ１１を動作主体とする。 [Operation of auction server]
Next, the operation of the auction server 100 configured as described above will be described. The operation is executed in cooperation with hardware such as the CPU 11 and the communication unit 19 of the auction server 100 and software stored in the storage unit 18. In the following description, for the sake of convenience, the CPU 11 is an operation subject.
　図４は、オークションサーバ１００による出品者推薦処理の流れを示したフローチャートである。 FIG. 4 is a flowchart showing the flow of exhibitor recommendation processing by the auction server 100.
　同図に示すように、オークションサーバ１００のＣＰＵ１１は、ユーザ端末２００から、出品者に対するアクションを示すリクエスト情報を受信したか否かを判断する（ステップ４１）。当該アクションリクエストを送信したユーザ端末２００のユーザを、以下、アクションユーザとも称する。 As shown in the figure, the CPU 11 of the auction server 100 determines whether or not request information indicating an action for the exhibitor has been received from the user terminal 200 (step 41). Hereinafter, the user of the user terminal 200 that has transmitted the action request is also referred to as an action user.
　ここで出品者に対するアクションとは、例えば、当該出品者をお気に入りに登録する処理の要求であるが、これに限られない。当該お気に入り登録要求は、例えば、オークションサイト上に掲載された特定のオークションページまたはオークション一覧ページからリンク可能な、出品者のプロフィールページにおいて、「この人をお気に入り登録」ボタンがユーザ端末２００のユーザにより押下されることでオークションサーバ１００へ送信される。 Here, the action for the exhibitor is, for example, a request for processing for registering the exhibitor as a favorite, but is not limited thereto. The favorite registration request is, for example, on the seller's profile page that can be linked from a specific auction page or auction list page posted on the auction site, and the “register this person as a favorite” button is displayed by the user of the user terminal 200. It is transmitted to the auction server 100 when pressed.
　上記アクション情報が受信されたと判断した場合（Ｙｅｓ）、ＣＰＵ１１は、上記アクション対象の出品者に関するデータを、上記出品情報データベース３１から取得する（ステップ４２）。 If it is determined that the action information has been received (Yes), the CPU 11 obtains data related to the action target exhibitor from the exhibition information database 31 (step 42).
　続いてＣＰＵ１１は、上記お気に入り登録情報データベース３３から、上記アクション対象の出品者をお気に入り登録している他のユーザ（入札者）が、当該アクション対象の出品者以外にお気に入り登録している他の出品者を、推薦候補として抽出する。この抽出処理では、アクションユーザが既にお気に入り登録している他の出品者は除外される。 Subsequently, the CPU 11 uses the favorite registration information database 33 to store other listings in which other users (bidders) who have registered the action target exhibitor as favorites are registered in addition to the action target exhibitor. Are extracted as recommendation candidates. In this extraction process, other exhibitors who are already registered as favorites by the action user are excluded.
　続いてＣＰＵ１１は、上記アクションユーザと、上記アクション対象の出品者をお気に入り登録している他のユーザ（入札者）との間の類似度を、上記類似度情報データベース３４を参照して算出する。 Subsequently, the CPU 11 calculates the similarity between the action user and another user (bidder) who has registered the seller as the action target with reference to the similarity information database 34.
　続いてＣＰＵ１１は、上記推薦候補をお気に入り登録している他の入札者から、上記アクションユーザとの間の類似度が所定値以上の入札者を抽出する（ステップ４５）。 Subsequently, the CPU 11 extracts bidders whose similarity with the action user is a predetermined value or more from other bidders who have registered the recommended candidates as favorites (step 45).
　続いてＣＰＵ１１は、上記抽出された推薦候補のうち、上記抽出された他の入札者がお気に入り登録している推薦候補を、推薦出品者として抽出する（ステップ４６）。 Subsequently, the CPU 11 extracts, as recommended exhibitors, recommended candidates registered as favorites by the other extracted bidders among the extracted recommended candidates (step 46).
　続いてＣＰＵ１１は、上記抽出された推薦出品者に関する情報を、上記出品情報データベース３１から取得し、それを、当該推薦出品者の抽出に利用された類似度に応じて優先付けすることで、推薦出品者一覧を生成する（ステップ４７）。 Subsequently, the CPU 11 acquires information on the extracted recommended exhibitor from the exhibition information database 31 and prioritizes it according to the degree of similarity used for extracting the recommended exhibitor. An exhibitor list is generated (step 47).
　ここで、当該類似度に応じた優先付けとは、例えば、類似度が高いほど、より上位に、または、一覧内の各推薦出品者の表示枠が大きくなるように、または、当該推薦出品者の出品商品の画像が多く表示されるように、上記一覧を生成することをいう。また、異なる推薦出品者について、上記類似度が同一の場合には、より多くの入札者にお気に入り登録されている（すなわち、より多くのファンを有する）出品者が優先される。 Here, the priority according to the degree of similarity is, for example, higher the degree of similarity, the higher the display frame of each recommended seller in the list, or the recommended seller. This means that the list is generated so that many images of the exhibited items are displayed. Further, when the above similarities are the same for different recommended exhibitors, an exhibitor who is registered as a favorite by more bidders (that is, has more fans) is given priority.
　続いてＣＰＵ１１は、上記アクション対象の出品者のデータと、上記生成した推薦出品者一覧とを含むウェブページを生成する（ステップ４８）。 Subsequently, the CPU 11 generates a web page including the data of the seller as the action target and the generated list of recommended sellers (step 48).
　そしてＣＰＵ１１は、上記生成されたウェブページを、上記ユーザのアクションに対する応答ページとしてユーザ端末２００へ送信する（ステップ４９）。 Then, the CPU 11 transmits the generated web page to the user terminal 200 as a response page for the user action (step 49).
　図５は、上記推薦処理と各ユーザとの関係を示した模式図である。 FIG. 5 is a schematic diagram showing the relationship between the recommendation process and each user.
　同図に示すように、アクションユーザＡがユーザ（出品者）Ｂにアクションを起こすと、当該ユーザＢをお気に入り登録している他のユーザ（入札者）、すなわち、ユーザＢのファンであるユーザＣ，Ｄ及びＥがお気に入り登録している他のユーザ（出品者）Ｆ，Ｇ，Ｈ，Ｉ，Ｊ及びＫが抽出される。 As shown in the figure, when the action user A takes action on the user (exhibitor) B, the other user (bidder) who has registered the user B as a favorite, that is, the user C who is a fan of the user B , D, and E are extracted as other users (exhibitors) F, G, H, I, J, and K.
　そして、当該ユーザＣ，Ｄ及びＥと、アクションユーザＡとの類似度が算出され、所定値以上の類似度を有するユーザＣ及びＤが抽出され、推薦候補のうち、当該ユーザＣ及びＤがお気に入り登録しているユーザＦ，Ｇ及びＨのみが推薦出品者としてユーザＡに推薦される。 Then, the degree of similarity between the users C, D and E and the action user A is calculated, the users C and D having a degree of similarity greater than or equal to a predetermined value are extracted, and among the recommended candidates, the users C and D are favorite Only registered users F, G and H are recommended to user A as recommended exhibitors.
　図６は、上記オークションサーバ１００によって生成されユーザ端末２００に表示される、推薦情報を含むウェブページの例を示した図である。 FIG. 6 is a diagram showing an example of a web page including recommendation information generated by the auction server 100 and displayed on the user terminal 200.
　ユーザ端末２００のユーザが、オークションサイト上の特定の出品者のプロフィールページ上で、「この人をお気に入り登録」ボタンを押すと、同図に示すお気に入り登録完了ページ６０がオークションサーバ１００からユーザ端末２００へ送信され表示される。 When the user of the user terminal 200 presses the “Register this person as a favorite” button on the profile page of a specific seller on the auction site, a favorite registration completion page 60 shown in FIG. Sent to and displayed.
　同図に示すように、お気に入り登録完了ページの例えば上部には、お気に入り登録完了通知領域６１が表示される。当該お気に入り登録完了通知領域６１には、ユーザがお気に入り登録を要求した出品者のお気に入り登録処理が完了した旨のメッセージが、当該出品者のプロフィール情報と共に表示される。 As shown in the figure, a favorite registration completion notification area 61 is displayed at the top of the favorite registration completion page, for example. In the favorite registration completion notification area 61, a message to the effect that the seller's favorite registration process for which the user requested the favorite registration has been completed is displayed together with the profile information of the seller.
　そして、例えば上記お気に入り登録完了通知領域６１の下部には、出品者推薦領域６２が表示される。 For example, an exhibitor recommendation area 62 is displayed below the favorite registration completion notification area 61.
　当該出品者推薦領域６２では、上記図５及び図６で示した所定値以上の類似度に基づく抽出処理によって抽出された推薦出品者の一覧が、それぞれの表示枠毎に表示される。 In the exhibitor recommendation area 62, a list of recommended exhibitors extracted by the extraction process based on the similarity equal to or higher than the predetermined value shown in FIGS. 5 and 6 is displayed for each display frame.
　各推薦出品者の表示枠には、その出品者のファンの数や評価値、その出品者が出品している商品の商品画像６３、当該出品者をお気に入り登録するためのお気に入り登録ボタン６４が表示される。商品画像６３は、当該商品画像６３を出品商品とする、開催中のオークションのページにハイパーリンクされている。 In the display frame of each recommended exhibitor, the number of fans of the exhibitor and the evaluation value, a product image 63 of the product that the exhibitor exhibits, and a favorite registration button 64 for registering the exhibitor as a favorite are displayed. Is done. The product image 63 is hyperlinked to a page of a held auction in which the product image 63 is an exhibited product.
　各推薦出品者の表示枠の面積は、上記類似度が高いほど、大きく設定され、また上部に表示される。同図の例では、ユーザ「ＡＡＡ」が、アクションユーザとの間で最も高い類似度を有する他の入札者にお気に入り登録されている出品者である。 The area of the display frame of each recommended exhibitor is set larger as the similarity is higher, and is displayed at the top. In the example shown in the drawing, the user “AAA” is an exhibitor who is registered as a favorite by another bidder who has the highest similarity with the action user.
　また、各推薦出品者の表示枠に表示される商品画像６３の数は、上記類似度が高いほど多く表示されるように設定される。例えば、上記ユーザ「ＡＡＡ」の表示枠においては、商品画像６３は５枚表示されているが、その下部の左側のユーザ「ＢＢＢ」の表示枠においては、商品画像６３は２枚しか表示されていない。さらに、その右側のユーザ「ＣＣＣ」及び「ＤＤＤ」の各表示枠においては、商品画像は表示されず、代わりに、商品画像６３へハイパーリンクされた出品商品表示ボタン６５が表示されている。 Also, the number of product images 63 displayed in the display frame of each recommended exhibitor is set so that the higher the similarity is, the more the product images 63 are displayed. For example, in the display frame of the user “AAA”, five product images 63 are displayed, but in the display frame of the user “BBB” on the lower left side, only two product images 63 are displayed. Absent. Furthermore, in each display frame of the users “CCC” and “DDD” on the right side, a product image is not displayed, and instead, an exhibition product display button 65 hyperlinked to the product image 63 is displayed.
　商品画像６３が複数表示される際、それらはランダムに抽出されるが、例えば上記類似度情報ＤＢ３４を基に、アクションユーザの商品名、メーカー、ジャンル等に関する嗜好度がより高い商品の画像が抽出されてもよい。 When a plurality of product images 63 are displayed, they are randomly extracted. For example, based on the similarity information DB 34, an image of a product with a higher degree of preference regarding the product name, manufacturer, genre, etc. of the action user is extracted. May be.
　また、上記商品画像６３を抽出する際、オークションサーバ１００は、入札情報データベース３２を基に、アクションユーザが既に落札したことのある商品を示す商品画像６３を抽出対象から除外する。 Further, when extracting the product image 63, the auction server 100 excludes the product image 63 indicating the product that the action user has already made a successful bid from the extraction target based on the bid information database 32.
　このように、本実施形態によれば、ユーザ端末２００のユーザ（入札者）が特定の出品者に対してアクションを起こした場合に、オークションサーバ１００は、当該アクションユーザと、上記特定の出品者をお気に入り登録している他の入札者がお気に入り登録している他の出品者を、アクションユーザと当該他の入札者との類似度を基にフィルタリングすることで、推薦候補の母数を絞って処理負荷を軽減しながら、そのアクションユーザが関心を持つ可能性が高いと思われる他の出品者を推薦することができる。 As described above, according to the present embodiment, when the user (bidder) of the user terminal 200 takes an action on a specific seller, the auction server 100 determines the action user and the specific seller. Narrow down the number of recommended candidates by filtering other sellers registered as favorites by other bidders based on the similarity between the action user and the other bidders. While reducing the processing load, other exhibitors who are likely to be interested in the action user can be recommended.
［変形例］
　本発明は上述の実施形態にのみ限定されるものではなく、本開示の要旨を逸脱しない範囲内において種々変更され得る。 [Modification]
The present invention is not limited to the above-described embodiments, and various modifications can be made without departing from the scope of the present disclosure.
　上述の実施形態においては、アクションユーザ（入札者）と、アクション対象の出品者をお気に入り登録している他のユーザ（出品者）との類似度が算出され、推薦候補のうち、アクションユーザとの間で所定値以上の類似度を有する他のユーザがお気に入りとして登録している出品者が推薦出品者として提示された。しかし、アクション対象の出品者と、推薦候補との類似度が算出され、推薦候補のうち、アクション対象の出品者と所定値以上の類似度を有する推薦候補が推薦出品者として提示されてもよい。 In the above-described embodiment, the similarity between the action user (bidder) and the other users (exhibitors) who have registered the seller to be acted on as favorites is calculated. An exhibitor who is registered as a favorite by another user having a similarity equal to or higher than a predetermined value is presented as a recommended exhibitor. However, the similarity between the action target exhibitor and the recommended candidate may be calculated, and among the recommended candidates, a recommendation candidate having a similarity greater than or equal to a predetermined value with the action target exhibitor may be presented as the recommended exhibitor. .
　図７は、この場合の推薦処理と各ユーザとの関係を示した模式図である。同図に示すように、アクションユーザＡがユーザ（出品者）Ｂにアクションを起こすと、当該ユーザＢをお気に入り登録している他のユーザ（入札者）、すなわち、ユーザＢのファンであるユーザＣ，Ｄ及びＥがお気に入り登録している他のユーザ（出品者）Ｆ，Ｇ，Ｈ，Ｉ，Ｊ及びＫが推薦候補として抽出される。 FIG. 7 is a schematic diagram showing the relationship between the recommendation process and each user in this case. As shown in the figure, when the action user A takes action on the user (exhibitor) B, the other user (bidder) who has registered the user B as a favorite, that is, the user C who is a fan of the user B Other users (exhibitors) F, G, H, I, J, and K with which, D, and E are registered as favorites are extracted as recommendation candidates.
　そして、当該推薦候補のユーザＦ，Ｇ，Ｈ，Ｉ，Ｊ及びＫと、アクション対象のユーザＢとの類似度が算出され、推薦候補のうち、所定値以上の類似度を有するユーザＦ，Ｈ及びＫのみが推薦出品者としてユーザＡに推薦される。 Then, the similarity between the recommendation candidates users F, G, H, I, J, and K and the action target user B is calculated, and among the recommendation candidates, the users F, H that have a similarity greater than or equal to a predetermined value. Only K and K are recommended to the user A as recommended exhibitors.
　要するに、入札傾向のユーザ同士の類似度も、出品傾向のユーザ同士の類似度も、同じ傾向のユーザ同士の類似度として推薦出品者の絞込みに用いられ得る。 In short, the degree of similarity between users who tend to tend and the degree of similarity between users who tend to exhibit can be used to narrow down recommended exhibitors as the degree of similarity between users who tend to have the same tendency.
　この場合、上記出品者推薦領域６２における各推薦出品者の表示枠の面積及び商品画像６３の数が、アクション対象の出品者と推薦候補との間の類似度に応じて可変されてもよい。 In this case, the area of the display frame of each recommended exhibitor and the number of product images 63 in the exhibitor recommended area 62 may be varied according to the degree of similarity between the seller who is the action target and the recommended candidate.
　上述の実施形態では、他の出品者を推薦するトリガとなる出品者に対するユーザ（入札者）のアクションとして、お気に入り登録が挙げられた。しかし、アクションはこれに限られない。例えば、ユーザが出品者のプロフィール情報の閲覧リクエスト、あるオークション（商品）のウォッチリスト（ユーザが入札はしていないが気になるオークションを登録するリスト）への登録リクエスト、出品者に対する評価付与リクエスト等、さまざまなアクションをトリガとして、それらアクションに対する応答結果と共に、同一のウェブページにおいて、他の出品者が推薦されてもよい。 In the above-described embodiment, favorite registration has been cited as an action of a user (bidder) for an exhibitor serving as a trigger for recommending other exhibitors. However, the action is not limited to this. For example, a user's request to view an exhibitor's profile information, a request to register a watch list for a certain auction (product) (a list for registering auctions that the user is not bidding but is worried about), and an evaluation grant request to the seller Other sellers may be recommended on the same web page, with various actions as triggers, along with the response results for those actions.
　上述の実施形態においては、所定期間（例えば数週間、１ヶ月等）以上、商品を出品していない出品者は、推薦候補から除外されてもよい。また同様に、他の入札者からの評価情報に基づいて、評価値が所定値以下またはマイナスの出品者が除外されてもよい。さらに、他の入札者のユーザ端末からオークションサーバの通知に基づいて生成されるブラックリストに登録されている出品者が除外されてもよい。 In the above-described embodiment, an exhibitor who has not exhibited a product for a predetermined period (for example, several weeks, one month, etc.) may be excluded from the recommended candidates. Similarly, an exhibitor whose evaluation value is equal to or less than a predetermined value may be excluded based on evaluation information from other bidders. Furthermore, the exhibitor registered in the black list generated based on the notification of the auction server from the user terminal of another bidder may be excluded.
　上述の実施形態においては、出品者推薦領域６２における各推薦出品者の表示枠の面積及び商品画像６３の数が、アクションユーザと他のユーザとの類似度に応じて可変された。しかし、オークションサーバ１００の負荷を考慮して、推薦対象の出品者のプロフィール情報ページや出品商品一覧ページへのページビューが所定数以上の場合には、商品画像６３の数が制限されるか、表示されなくてもよいし、商品画像へハイパーリンクされた出品商品表示ボタン６５が表示されなくてもよい。 In the above-described embodiment, the area of the display frame of each recommended exhibitor and the number of product images 63 in the exhibitor recommended area 62 are varied according to the similarity between the action user and other users. However, in consideration of the load of the auction server 100, if the number of page views to the profile information page or the listing item list page of the seller to be recommended is a predetermined number or more, the number of the product images 63 is limited, It may not be displayed, and the exhibition product display button 65 hyperlinked to the product image may not be displayed.
　上述の実施形態においては、本発明がオークションシステムに適用された例が示されたが、例えば電子商店街等の他のサービスに適用されてもよい。すなわち、購買者となり得るユーザと、販売者となり得るユーザとがそれぞれのユーザ端末を介してサーバに接続することで成立する売買システム全般に本発明が適用可能である。 In the above-described embodiment, an example in which the present invention is applied to an auction system is shown, but it may be applied to other services such as an online shopping mall, for example. In other words, the present invention can be applied to any trading system that is established by connecting a user who can be a buyer and a user who can be a seller to a server via each user terminal.
　１１…ＣＰＵ
　１８…記憶部
　１９…通信部
　３１…出品情報データベース
　３２…入札情報データベース
　３３…入り登録情報データベース
　３４…類似度情報データベース
　５０…インターネット
　６０…お気に入り登録完了ページ
　６１…お気に入り登録完了通知領域
　６２…出品者推薦領域
　６３…商品画像
　６４…お気に入り登録ボタン
　１００…オークションサーバ
　２００…ユーザ端末 11 ... CPU
DESCRIPTION OF SYMBOLS 18 ... Memory | storage part 19 ... Communication part 31 ... Exhibition information database 32 ... Bid information database 33 ... Enter registration information database 34 ... Similarity information database 50 ... Internet 60 ... Favorite registration completion page 61 ... Favorite registration completion notification area 62 ... Exhibitor Recommended area 63 ... Product image 64 ... Favorite registration button 100 ... Auction server 200 ... User terminal

Claims (7)

  1. 　商品またはサービスの売買ウェブサイトにおける販売ユーザと当該販売ユーザをお気に入りとして登録している購買ユーザとの関係を示すお気に入り情報と、前記販売ユーザに販売された商品またはサービスと前記購買ユーザによって購買された商品またはサービスとの類似度に関する類似度情報とを記憶する記憶部と、
    　前記販売ユーザの端末及び前記購買ユーザの端末と通信可能な通信部と、
    　前記売買ウェブサイトを介した第１の購買ユーザから第１の販売ユーザに対する所定のアクションを示すアクション情報が前記第１の購買ユーザの端末から前記通信部により受信された場合に、前記お気に入り情報を基に、前記第１の販売ユーザをお気に入り登録している第２の購買ユーザがお気に入り登録している第２の販売ユーザを抽出し、前記類似度情報を基に、前記第１の販売ユーザと前記第２の販売ユーザとの類似度または前記第１の購買ユーザと前記第２の購買ユーザとの類似度を算出し、前記抽出された第２の販売ユーザのうち、前記第１の販売ユーザとの間で所定値以上の類似度を有するか、または、前記第１の購買ユーザと所定値以上の類似度を有する第２の購買ユーザがお気に入りとして登録している第３の販売ユーザを抽出し、当該第３の販売ユーザを推薦する推薦情報を含むウェブページを、前記第１の購買ユーザの端末へ送信するように前記通信部を制御可能な制御部と
    　を具備する情報処理装置。     Favorite information indicating the relationship between a sales user on a website for selling or selling goods or services and a purchasing user who has registered the selling user as a favorite, and the goods or services sold to the selling user and purchased by the purchasing user A storage unit that stores similarity information related to the similarity to a product or service;
    A communication unit capable of communicating with the terminal of the selling user and the terminal of the purchasing user;
    When the action information indicating a predetermined action from the first purchasing user via the trading website to the first selling user is received from the terminal of the first purchasing user by the communication unit, the favorite information is Based on the similarity information, the second purchase user who has registered the first sales user as a favorite is extracted as a second purchase user. The similarity between the second sales user or the similarity between the first purchase user and the second purchase user is calculated, and the first sales user among the extracted second sales users is calculated. A third sales user who has a similarity greater than or equal to a predetermined value or a second purchase user who has a similarity greater than or equal to the first purchase user and is registered as a favorite Extracted, the third web page including a recommendation information for recommending selling user, the information processing apparatus and a control capable of controlling unit the communication unit to transmit to a terminal of the first purchase the user.
  2. 　請求項１に記載の情報処理装置であって、
    　前記制御部は、前記第１の購買ユーザの端末によって表示可能な前記推薦情報の表示欄の一覧を含むウェブページを、前記第１の販売ユーザと前記第２の販売ユーザとの間の類似度または前記第１の購買ユーザと前記第２の購買ユーザとの間の類似度が高いほど、当該類似度に基づいて推薦される第３の販売ユーザの推薦情報の表示欄が前記一覧内で優先的に表示されるように生成する
    　情報処理装置。     The information processing apparatus according to claim 1,
    The control unit displays a web page including a list of display fields of the recommended information that can be displayed by the terminal of the first purchase user, and the similarity between the first sales user and the second sales user. Alternatively, the higher the similarity between the first purchase user and the second purchase user, the higher priority is given to the recommendation information display field of the third sales user recommended based on the similarity in the list. An information processing device that generates the information so that it is displayed.
  3. 　請求項２に記載の情報処理装置であって、
    　前記推薦情報の表示欄は、前記第３の販売ユーザが販売している商品を示す画像を含み、
    　前記制御部は、前記第１の販売ユーザと前記第２の販売ユーザとの間の類似度または前記第１の購買ユーザと前記第２の購買ユーザとの間の類似度が高いほど、当該類似度に基づいて推薦される第３の販売ユーザの商品を示す画像の数が多くなるように前記ウェブページを生成する
    　情報処理装置。     An information processing apparatus according to claim 2,
    The display column of the recommendation information includes an image indicating a product sold by the third sales user,
    The control unit increases the similarity between the first selling user and the second selling user or the similarity between the first purchasing user and the second purchasing user. The information processing apparatus that generates the web page so that the number of images indicating the products of the third sales user recommended based on the degree increases.
  4. 　請求項２に記載の情報処理装置であって、
    　前記推薦情報の表示欄は、前記第３の販売ユーザが販売している商品を示す画像を含み、
    　前記制御部は、前記画像として、前記第１の購買ユーザが購買した商品以外の商品の画像が含まれるように前記ウェブページを生成する
    　情報処理装置。     An information processing apparatus according to claim 2,
    The display column of the recommendation information includes an image indicating a product sold by the third sales user,
    The control unit generates the web page so that an image of a product other than a product purchased by the first purchase user is included as the image.
  5. 　請求項１に記載の情報処理装置であって、
    　前記制御部は、前記第１の販売ユーザと前記第２の販売ユーザとの間の類似度または前記第１の購買ユーザと前記第２の購買ユーザとの間の類似度が、抽出された複数の第３の販売ユーザに関して同一の場合、当該複数の第３の販売ユーザをそれぞれお気に入りとして登録している購買ユーザの数を比較し、より多くの購買ユーザに登録されている第３の販売ユーザの推薦情報の表示欄が前記一覧内で優先的に表示されるように前記ウェブページを生成する
    　情報処理装置。     The information processing apparatus according to claim 1,
    The control unit extracts a plurality of extracted similarities between the first selling user and the second selling user or similarities between the first purchasing user and the second purchasing user. If the third sales user is the same, the number of purchase users who have registered the plurality of third sales users as favorites respectively is compared, and the third sales user registered to more purchasing users. An information processing apparatus that generates the web page so that a display field of recommended information is preferentially displayed in the list.
  6. 　商品またはサービスの売買ウェブサイトにおける販売ユーザと当該販売ユーザをお気に入りとして登録している購買ユーザとの関係を示すお気に入り情報と、前記販売ユーザに販売された商品またはサービスと前記購買ユーザによって購買された商品またはサービスとの類似度に関する類似度情報とを記憶し、
    　前記売買ウェブサイトを介した第１の購買ユーザから第１の販売ユーザに対する所定のアクションを示すアクション情報が前記第１の購買ユーザの端末から受信された場合に、前記お気に入り情報を基に、前記第１の販売ユーザをお気に入り登録している第２の購買ユーザがお気に入り登録している第２の販売ユーザを抽出し、
    　前記類似度情報を基に、前記第１の販売ユーザと前記第２の販売ユーザとの類似度または前記第１の購買ユーザと前記第２の購買ユーザとの類似度を算出し、
    　前記抽出された第２の販売ユーザのうち、前記第１の販売ユーザとの間で所定値以上の類似度を有するか、または、前記第１の購買ユーザと所定値以上の類似度を有する第２の購買ユーザがお気に入りとして登録している第３の販売ユーザを抽出し、
    　前記第３の販売ユーザを推薦する推薦情報を含むウェブページを、前記第１の購買ユーザの端末へ送信する
    　情報処理方法。     Favorite information indicating the relationship between a sales user on a website for selling or selling goods or services and a purchasing user who has registered the selling user as a favorite, and the goods or services sold to the selling user and purchased by the purchasing user Memorizing similarity information on similarity to goods or services,
    When action information indicating a predetermined action for the first selling user from the first purchasing user via the trading website is received from the terminal of the first purchasing user, based on the favorite information, the Extracting a second sales user favorite registered by a second purchasing user who has registered the first sales user as a favorite;
    Based on the similarity information, the similarity between the first sales user and the second sales user or the similarity between the first purchase user and the second purchase user is calculated,
    Among the extracted second sales users, the first sales user has a similarity greater than or equal to a predetermined value, or has a similarity greater than or equal to a predetermined value with the first purchase user. The third sales user registered as a favorite by two purchasing users is extracted,
    An information processing method for transmitting a web page including recommendation information for recommending the third sales user to the terminal of the first purchase user.
  7. 　情報処理装置に、
    　商品またはサービスの売買ウェブサイトにおける販売ユーザと当該販売ユーザをお気に入りとして登録している購買ユーザとの関係を示すお気に入り情報と、前記販売ユーザに販売された商品またはサービスと前記購買ユーザによって購買された商品またはサービスとの類似度に関する類似度情報とを記憶するステップと、
    　前記売買ウェブサイトを介した第１の購買ユーザから第１の販売ユーザに対する所定のアクションを示すアクション情報が前記第１の購買ユーザの端末から受信された場合に、前記お気に入り情報を基に、前記第１の販売ユーザをお気に入り登録している第２の購買ユーザがお気に入り登録している第２の販売ユーザを抽出するステップと、
    　前記類似度情報を基に、前記第１の販売ユーザと前記第２の販売ユーザとの類似度または前記第１の購買ユーザと前記第２の購買ユーザとの類似度を算出するステップと、
    　前記抽出された第２の販売ユーザのうち、前記第１の販売ユーザとの間で所定値以上の類似度を有するか、または、前記第１の購買ユーザと所定値以上の類似度を有する第２の購買ユーザがお気に入りとして登録している第３の販売ユーザを抽出するステップと、
    　前記第３の販売ユーザを推薦する推薦情報を含むウェブページを、前記第１の購買ユーザの端末へ送信するステップと
    　を実行させるプログラム。     In the information processing device,
    Favorite information indicating the relationship between a sales user on a website for selling or selling goods or services and a purchasing user who has registered the selling user as a favorite, and the goods or services sold to the selling user and purchased by the purchasing user Storing similarity information relating to the similarity to the product or service;
    When action information indicating a predetermined action for the first selling user from the first purchasing user via the trading website is received from the terminal of the first purchasing user, based on the favorite information, the Extracting a second sales user favorite registered by a second purchasing user who has registered the first sales user as a favorite;
    Calculating the similarity between the first sales user and the second sales user or the similarity between the first purchase user and the second purchase user based on the similarity information;
    Among the extracted second sales users, the first sales user has a similarity greater than or equal to a predetermined value, or has a similarity greater than or equal to a predetermined value with the first purchase user. Extracting a third sales user registered as a favorite by two purchasing users;
    Transmitting a web page including recommendation information for recommending the third sales user to the terminal of the first purchasing user.
PCT/JP2014/057447 2014-03-19 2014-03-19 Information processing device, information processing method, and program WO2015140947A1 (en)

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