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WO2000062223A1 - Systemes et procedes d'assistance aux utilisateurs pour choisir et evaluer des articles dans un catalogue electronique - Google Patents

Systemes et procedes d'assistance aux utilisateurs pour choisir et evaluer des articles dans un catalogue electronique Download PDF

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Publication number
WO2000062223A1
WO2000062223A1 PCT/US2000/040077 US0040077W WO0062223A1 WO 2000062223 A1 WO2000062223 A1 WO 2000062223A1 US 0040077 W US0040077 W US 0040077W WO 0062223 A1 WO0062223 A1 WO 0062223A1
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WIPO (PCT)
Prior art keywords
community
users
user
items
communities
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PCT/US2000/040077
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English (en)
Inventor
Jeffrey P. Bezos
Joel R. Spiegel
Jon Mcauliffe
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Amazon com Inc
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Amazon com Inc
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Publication date
Priority claimed from US09/377,447 external-priority patent/US6963850B1/en
Priority claimed from US09/377,322 external-priority patent/US7082407B1/en
Application filed by Amazon com Inc filed Critical Amazon com Inc
Priority to AU47150/00A priority Critical patent/AU4715000A/en
Publication of WO2000062223A1 publication Critical patent/WO2000062223A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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

Definitions

  • This invention relates to electronic commerce and information filtering. More specifically, this invention relates to information processing methods for assisting online users in identifying and evaluating items from a database of items based on user purchase histories or other historical data. BACKGROUND OF THE INVENTION
  • Web sites of online merchants commonly provide various types of informational services for assisting users in evaluating the merchants' product offerings. Such services can be invaluable to an online customer, particularly if the customer does not have the opportunity to physically inspect the merchants' products or talk to a salesperson.
  • One type of service involves recommending products to users based on personal preference information.
  • preference information may be specified by the user explicitly, such as by filling out an online form, or implicitly, such as by purchasing or rating products.
  • the personalized product recommendations may be communicated to the customer via an email message, a dynamically-generated Web page, or some other communications method.
  • Collaborative filtering methods operate by identifying other users with similar tastes, and then recommending products that were purchased or highly rated by such similar users.
  • Content-based filtering methods operate by processing product-related content, such as product descriptions stored in a database, to identify products similar to those purchased or highly rated by the user. Both types of methods can be combined within a single system.
  • Web sites also commonly implement services for collecting and posting subjective and objective information about the product tastes of the online community.
  • the Web site of Amazon.com provides a service for allowing users to submit ratings (on a scale of 1-5) and textual reviews of individual book, music and video titles.
  • ratings on a scale of 1-5
  • textual reviews of individual book, music and video titles.
  • the user is presented with a product detail page that includes the title's average rating and samples of the submitted reviews.
  • Users of the site can also access lists of the bestselling titles within particular product categories, such as "mystery titles" or "jazz CDs.”
  • the present invention addresses these and other problems by providing various computer-implemented services for assisting users in identifying and evaluating items that have gained acceptance within particular user communities.
  • the services are preferably implemented as part of a Web site system, but may alternatively be implemented as part of an online services network, interactive television system, or other type of information system.
  • the services are provided on the Web site of an online store to assist users in identifying and evaluating products, such as book titles.
  • the communities may include explicit membership communities that users can join through a sign-up page.
  • the explicit membership communities may include, for example, specific universities, outdoors clubs, community groups, and professions. Users may also have the option of adding explicit membership communities to the system, including communities that are private (not exposed to the general user population).
  • the communities may additionally or alternatively include implicit membership communities for which membership is determined without any active participation by users. Examples of implicit membership communities include domain-based communities such as Microsoft.com Users (determined from users' email addresses), geographic region based communities such as New Orleans Area Residents
  • a service for automatically generating and displaying community-based popular items lists.
  • the popular items lists are preferably in the form of bestseller lists that are based on sales activities over a certain period of time, such as the last two months. By viewing these lists, users can readily identify the bestselling products within specific communities.
  • the bestseller lists for the communities of which the user is a member are automatically displayed on a personalized Web page.
  • the bestseller lists could also be communicated by email, fax, or another communications method.
  • One feature of the invention involves generating bestseller lists that are based solely on Internet domains, without requiring any active user participation. These domain-based bestseller lists may be displayed automatically on the home page or other area of the Web site.
  • a service for notifying users interested in particular products of other users that have purchased the same or similar products.
  • the service is implemented by providing user contact information on product detail pages. For example, when a user views a product detail page for a particular product (such as a kayak), the detail page may be customized to include the names and email addresses of other members of the user's community (such as a kayaking club) that recently purchased the same product. If any of these other members is online, the user may be presented the option to send an instant message or otherwise chat online with such members.
  • users can opt to expose their contact information to other community members (and thus participate in the service) on a community-by-community basis.
  • a variation of this service involves notifying users interested in particular merchants (e.g., sellers on an online auction site) of the contact information of other users (preferably fellow community members) that have engaged in business with such merchants.
  • characterizing purchases of a community, and these characterizing purchases are used to recommend items within that community. Specifically, the purchase history data of the community is compared to the purchase history data of a general user population to identify a set of items purchased within the community that distinguish the community from the general user population. Items are then implicitly or explicitly recommended to members of the community from this set, such as through popular items lists or email notifications.
  • each viewing, downloading and/or rating event can be treated the same as a purchase event.
  • Figure 1 illustrates an example sign-up page for specifying community memberships and service preferences
  • Figure 2 illustrates a personalized community bestsellers page
  • Figure 3 illustrates an example product (book) detail page which includes contact information of other community members that have purchased the product
  • Figure 4 illustrates an example hotseller notification email message
  • Figure 5 is an architectural drawing which illustrates a set of components which may be used to implement the community bestseller lists, hotseller notification, and contact information exchange services;
  • Figure 6 illustrates an offline process for generating the community bestseller lists table and the product-to- member tables of Figure 5;
  • Figure 7A and 7B illustrate an online (real time) process for generating personalized community bestseller pages of the type shown in Figure 2.
  • Figure 8 illustrates an online process for generating personalized product detail pages of the type shown in Figure 3.
  • Figure 9 illustrates an offline process for generating email notifications of hotselling products as in Figure 4.
  • Figure 10 illustrates a process for notifying community members of purchases made within the community.
  • the Community Interests services will be described primarily in the context of a hypothetical system for assisting users of a merchant Web site, such as the Web site of Amazon.com, in locating and evaluating book titles within an electronic catalog. It will be recognized, however, that the services and their various features are also applicable to the marketing and sales of other types of items.
  • the items that are the subject of the services could be cars sold by an online car dealer, movies titles rented by an online video store, computer programs or informational content electronically downloaded to users' computers, or stock and mutual fund shares sold to online investors.
  • the "purchases" referred to herein need not involve an actual transfer of ownership, but could rather involve leases, licenses, rentals, subscriptions and other types of business transactions.
  • the hypothetical Web site provides various services for allowing users to browse, search and make purchases from a catalog of several million book, music and video titles. It is also assumed that information about existing customers of the site is stored in a user database, and that this information typically includes the names, shipping addresses, email addresses, payment information and purchase histories of the customers. The information that is stored for a given customer is referred to collectively as the customer's "user pro ile.”
  • the system may also use implicit membership communities for which membership is based in-whole or in-part on the purchase activities of the users.
  • the implicit membership community "fishermen" may include all users that have purchased a book about fishing.
  • the communities may be defined or inferred from such purchase histories using clustering techniques.
  • the Community Interests system includes four different types of services.
  • the first referred to herein as “Community Books,” involves generating and displaying lists of the bestselling titles within specific communities. Using this feature, users can identify the book titles that are currently the most popular within their own communities and/or other communities.
  • the bestselling titles are preferably identified based on the numbers of units sold, but could additionally or alternatively be based on other sales related criteria.
  • the lists may be based in-whole or in-part on other types of data, such as user viewing activities or user submissions of reviews and ratings.
  • the second service involves informing a user that is viewing a particular product of other users within the same community that have purchased the same or a similar product.
  • the page may include the names and email addresses of other Netscape.com users that have recently purchased the title, and/or an instant messaging box for sending a message to any such user that is currently online.
  • their names and/or email addresses may be masked, in which case an email alias or a bulletin board may be provided for communicating anonymously.
  • This feature may also be used to display the contact information of other users that have bought from or otherwise conducted business with a particular seller.
  • the third service referred to as "Hotseller Notification,” automatically notifies users of titles that have become unusually popular within their respective communities. For example, a user within a particular hiking club might be notified that several other users within his club have recently purchased a new book on local hiking trails.
  • a community's "hotsellers” are identified by comparing, for each title on the community's bestseller list, the title's popularity within the community to the title's popularity within the general user population.
  • the popularities of the titles are preferably based at least in-part on numbers of units sold , but may be additionally or alternatively be based other types of criteria such as user viewing activities or user submissions of reviews and ratings.
  • a fourth service referred to as "Purchase Notification,” automatically notifies users of purchases (including titles and the contact information of the purchaser) made within their respective communities.
  • This service may, for example, be made available as an option where the community members have all agreed to share their purchase information. Alternatively, users may have the option to expose their purchases to other community members on a user-by-user and/or item-by-item basis.
  • Figure 1 illustrates the general form of a sign-up page that can be used to enroll with the Community Interests services.
  • Community Interests services can be implemented without requiring any active participation by the site's users.
  • all four services could be based solely on the Internet domains of the users, without requiring users to actively join communities.
  • the communities could be defined automatically based on correlations between purchases; for example, all users that purchased more than X books within the "Business and Investing" category could automatically be assigned to a Business and Investing community.
  • the sign-up page includes drop-down lists 30 for allowing the user to specify membership in one or more explicit membership communities.
  • the communities that are presented to the user are those that are currently defined within the system. As described below, new communities may be added by system administrators, regular users, or both.
  • the drop-down lists 30 may be filtered lists that are generated based on information known about the particular user. For example, the selections presented in the "local community groups" and "local outdoors clubs” lists may be generated based on the user's shipping address.
  • Any of a variety of other interface methods could be used to collect community membership information from users. For example, rather that having the user select from a drop-down list, the user could be prompted to type-in the names of the communities to which the user belongs. When a typed-in name does not match any of the names within the system, the user may be presented with a list of "close matches" from which to choose. Users may also be provided the option of viewing the membership lists of the communities and specifying the users with which to share information.
  • users may also be given the opportunity to add new communities to the system.
  • a user wishing to add a new community has the option of designating the community as "private," meaning that the community's existence and/or data will not be exposed to the general public.
  • Private communities may be useful, for example, when a closed group of users wishes to privately share information about its purchases.
  • the user may, for example, be prompted to enter the email addresses of prospective members, in which case the system may automatically send notification emails to such users.
  • companies and organizations may be provided the option of designating their domain- based communities as private.
  • the user may be asked certain questions that pertain to the selected communities, such as university graduation dates and majors.
  • the user may also be prompted to enter authentication information that is specific to one or more of the selected communities. For example, the user may be asked to enter a community password (even if the community is not private), or may be asked a question that all members of the group are able answer.
  • a community may also have a designated "group administrator" that has the authority to remove unauthorized and disruptive users from the group.
  • FIG. 2 illustrates the general form of a personalized Web page (referred to herein as the "community bestsellers page") which may be used to display the community bestseller lists.
  • This page may be accessed, for example, by selecting a link from the site's home page.
  • Community bestseller lists could additionally or alternatively be provided on other areas of the site.
  • the bestseller list of the Nasa.com domain could automatically be displayed on the home page for any user that has purchased a book on space exploration; or, when a user from the domain mckinsey.com makes a purchase, the user might be presented the message "would you like to see the bestsellers from the McKinsy & Co. group?"
  • the user is a member of the explicit membership community Cascade
  • the user may also be provided the option of viewing the bestseller lists of other communities, including communities of which the user is not a member.
  • the listing of other communities may be ordered according to the known or predicted interests of the user.
  • a community directory structure or search engine may also be provided for assisting users in finding communities and their bestseller lists.
  • a user can be a member of a composite community only through membership in one of that composite community's member, base communities.
  • a "base community,” as used herein, is any non-composite community, regardless of whether it is part of a composite community.)
  • the composite communities that are exposed to the general user population could be defined by system administrators; alternatively, the composite communities could be defined automatically, such as by grouping together all base communities that have certain keywords in their titles.
  • users can also define their own, "personal" composite communities, such as by selecting from a list (not shown) of base communities and assigning a community name.
  • a user could, for example, define a composite community which consists of all kayaking clubs on the West Coast or of a selected group of hi-tech companies. If the user has defined a personal composite community, that community's bestseller list is preferably automatically displayed on the user's community bestsellers page ( Figure 2).
  • the definitions of any personal composite communities specified by the user may be stored within a cookie on the user's machine.
  • users can also view a bestseller list of the general user population (e.g., all Amazon.com users).
  • the general user population is treated as special type of community (i.e., it is neither a base community nor a composite community), and is referred to herein as the "global community.”
  • Another option involves allowing users to specify subsets of larger communities using demographic filtering. For example, a user within the MIT community might be given the option to view the bestselling titles among MIT alumnus who fall within a particular age group or graduated a particular year.
  • Figure 3 depicts an example product (book) detail page which illustrates one possible form of the Contact Information Exchange service.
  • Detail pages of the type shown in Figure 3 can be located using any of a variety of navigation methods, including performing a book search using the site's search engine or navigating a subject-based browse tree.
  • the contact information 58 of other community members that purchased the displayed book title (preferably within a certain period of time), or possibly similar titles, is displayed at the bottom of the page. In other embodiments, the contact information may be displayed without regard to community membership.
  • the contact information 58 includes the name, email address and common communities of the users, although telephone numbers, residence addresses, chat boxes and other types of contact information could additionally or alternatively be included.
  • the user viewing the book detail page might contact such other users to ask their opinions about the book, or about the bike tours described therein.
  • the contact information might be useful for arranging a group trip.
  • the page may also include a link 60 or other type of object for sending an email or other message to the fellow community member.
  • the system uses well known methods to determine whether any of these other users is currenly online. If one or more of the prior purchasers is online, the user is presented an option to send an instant message to prior purchaser(s), and/or to set up a private chat room for communicating with prior purchasers.
  • the contact information may simply be in the form of an instant messaging box or other option for chatting online with specific users.
  • the various contact information exchange features may be used to assist users in evaluating the reputation of a particular merchant. For example, when a user views an auction of a particular seller, the contact information of other community members (or possibly non-community members) that bought from that seller may be displayed, or an option could be provided to chat with any such users that are currently online. Where the merchant has its own Web site, the contact information could, for example, be displayed as Web site metadata using a browser add-on of the type provided by Aiexa Internet of San Francisco, California.
  • the email addresses of the contacts could be special aliases created for communicating anonymously (in which case the prospective purchaser may similarly be assigned an email alias for the contacts to respond), or the prospective purchaser and the contacts could be given a link to a private bulletin board
  • Figure 4 illustrates an example of an email document which may be used to notify community members of a hotselling book title. Similar notifications may be provided to users through customized Web pages and other communications methods.
  • the email document is preferably sent to all participating members of the community that have not already purchased the book.
  • the email document includes a textual description 66 which, among other things, includes a synopsis of the book title and informs the user of the level of acceptance the title has attained within the community.
  • the description also includes a h ⁇ pertextual link 68 to the title's detail page on the site.
  • the email document preferably includes a listing 70 of the contact information of other community members that have purchased the book.
  • Email notifications sent by the Purchase Notification service may likewise include a synopsis of the purchased product and a link to the product's detail page.
  • the email document may include the purchaser's contact information (and possibly the contact information of other community members who have purchased the product); for example, when User A in Community A purchases an item, an email may be sent to other members of Community A with a description of the product and User A's contact information.
  • FIG. 5 illustrates a set of Web site system components that may be used to implement the above-described features.
  • the Web site system includes a Web server 76 which accesses a database 78 of HTML (Hypertext Markup Language) and related content.
  • the HTML database 78 contains, among other things, the basic HTML documents used to generate the personalized sign-up, community bestsellers, and product detail pages of Figures 1-3.
  • the Web server 76 accesses service code 80, which in-tur ⁇ accesses a user database 82, a community database 84, a bibliographic database of product data (not shown), and a database or other repository of community data 86.
  • the various databases are shown separately in Figure 1 for purposes of illustration, but may in practice be combined within one or more larger database systems.
  • the service code 80 and other executable components may, for example, run on one or more Unix or Windows NT based servers and/or workstations.
  • the community data 86 includes a "community bestseller lists" table 86A which contains, for the global community and each base community, a listing of the currently bestselling book titles. In some implementations, the listing for the global community is omitted.
  • each entry 88 in each bestseller list includes: (a) the product ID (ProdlD) of a book title, and (b) a count value which represents, for a given time window, the number of copies purchased by members of the community.
  • the product IDs may be assigned or processed such that different media formats (e.g., paperback, hardcover, and audio tape) of the same title are treated as the same item.
  • the community bestseller lists table 86A is used both for the generation of bestseller lists and the generation of hotseller notifications.
  • the community database 84 contains information about each base community (including both explicit and implicit membership base communities when both types are provided) that exists within the system. This information may include, for example, the community name, the type of the community (e.g., college/university, local community group, etc.), the location (city, state, country, etc.) of the community, whether the community is private, whether the community participates in the Purchase Notification service, any authentication information required to join the community, and any community policies (e.g., by joining, all users agree to expose their purchases to other members).
  • the database 84 may also include information about the user database conditions which give rise to membership. As indicated above, the information stored within the communities database 84 may be generated by end users, system administrators, or both.
  • the second process 80B is an online process which is used to generate personalized community bestsellers pages of the type shown in Figure 2.
  • the third process 80C is an online process which is used to generate product detail pages with contact information as shown in Figure 3; and which may also be used to compile contact information to be displayed within notification emails of the type shown in Figure 4.
  • the fourth process 80D is an offline process which is used to identify and notify users of hotselling products within specific communities.
  • the fifth process 80E is used to implement the Purchase Notification service.
  • step 102 the retrieved purchase histories are processed to build a list of all products that were purchased within the last N days.
  • this list includes any products that were purchased solely by global community members, and thus is not limited to base community purchases.
  • step 104 the process uses the data structures obtained from steps 100 and 102 to generate a temporary purchase count array 104A.
  • Each entry in the array 104A contains a product count value which indicates, for a corresponding community: product pair, the number of times the product was purchased by a member of the community in the last N days.
  • the array 104A shown in Figure 6 indicates that a total of 350 users purchased product "PR0D1," and that three of those purchases came from base community "BASE_1.”
  • a pseudocode listing of a routine that can be used to generate the array is shown in Table 1. Multiple purchases of the same product by the same user are preferably counted as a singe purchase when generating the array.
  • step 106 the data stored in the array is used to generate the community bestseller lists. This task involves, for each base community and the global community, forming a list of the purchased products, sorting the list according to purchase counts, and then truncating the list to retain only the X (e.g., 100) top selling titles. A longer bestsellers list (e.g., the top selling 10,000 titles) may be generated for the global community, as is desirable for identifying community hotsellers.
  • the bestseller lists are written to a table 86A of the type depicted in Figure 5, and the new table replaces any existing table.
  • the bestsellers lists of base communities that have less than a pre-specified threshold of total sales (e.g., less than 5) may optionally be omitted from the table 86A.
  • Good lists for the composite communities defined by system administrators could also be generated as part of the Figure 6 process, or could be generated "on-the-f ly" as described below.
  • the last two steps 108, 110 of Figure 6 are used to generate the product-to-member mapping tables 86B of Figure 5.
  • the first step 108 of this process involves generating a temporary table (not shown) which maps base communities to corresponding members that have opted to participate in the Contact Information Exchange program ("participating members").
  • this temporary table and the purchase histories of the participating members are used to generate the product-to-member mapping table 86B for each base community.
  • the contact information of the participating members may also be stored in these tables 86B to reduce accesses to the user database 82.
  • a separate table 86B is preferably generated for each base community, a single table or other data structure could be used.
  • Such data may include, for example, "click-through” events to product detail pages, “add to shopping cart” events, and product ratings and reviews submitted by users.
  • Figures 7A and 7B illustrate the steps that are performed by the community bestseller processing code 80B to generate personalized community bestseller pages of the type shown in Figure 2.
  • the first step 120 in Figure 7A involves generating a list of the communities for which bestseller lists are to be generated and displayed. If the user has already selected one or more communities from the drop down box 50 ( Figure 2), these selected communities are included in this list. If the user's identity is known, the user's base communities and personal composite communities, if any, may be added to this list. If the list is empty at this point, a set of default communities may used. User identities are preferably determined using browser cookies, although a login procedure or other authentication method could be used. In other implementations, the community bestseller lists may be displayed without regard to the user's community membership profile.
  • the next step 124 involves generating the bestseller lists for each of the selected communities. This process is illustrated by Figure 7B and is described below.
  • the process identifies any communities that are related to the user's base communities, so that these related communities can be displayed within or at the top of the drop-down list 50 ( Figure 2). Any composite community which includes one of the user's base communities may automatically be included in this list.
  • information stored in the community database 84 may be used to identify related base communities. In other implementations, this step 124 may be omitted.
  • step 128 the bestseller lists and the list of related communities are incorporated into the community bestsellers page.
  • the community's bestseller list is simply retrieved from the table 86A (step 136). Otherwise, the bestseller lists of all of the composite community's member base communities are retrieved and merged (steps 138-142) to form the bestseller list.
  • the product count values could optionally be converted to normalized score values (step 138) so that those communities with relatively large sales volumes will not override those with smaller sales volumes. For a given product within a given bestseller list, the score may be calculated as (product's purchase count)/(total purchase count of bestseller list).
  • the lists are then merged while summing scores of like products (step 140), and the resulting list is sorted from highest to lowest score (step 142). If the composite community is one that has been defined by system administrators (as opposed to a personal composite community defined by the user), the resulting bestseller list may be added to the table 86A or otherwise cached in memory to avoid the need for regeneration.
  • one optional feature involves filtering out from the bestseller list some or all of the products that exist within the global community's bestseller list. For example, any book title that is within the top 500 bestseller's of the general population may automatically be removed. Alternatively, such titles could be moved to a lower position within the list.
  • This feature has the effect of highlighting products for which a disparity exists between the product's popularity within the global community versus the community for which the bestseller list is being generated. This feature may be provided as an option that can be selectively enabled or invoked by users. Products could additionally or alternatively be filtered out based a comparison of the product's velocity or acceleration within the particular community to the product's velocity or acceleration within the global community.
  • the bestseller list is truncated (such as by taking the top 10 entries) and then returned to the process of Figure 7A for incorporation into the Web page.
  • the Figure 7B process is repeated for each community to be included within the community bestsellers page.
  • Figure 8 illustrates the steps that are performed by the product detail page process 80C to generate detail pages (as in Figure 3) for participants in the Contact Information Exchange program.
  • product detail pages can be accessed using any of the site's navigation methods, such as conducting a search for a title.
  • step 150 a list of the base communities of which the user is a member is obtained — either from a browser cookie or from the user database 82. For each base community in this list, that community's product-to-member mapping table 86B ( Figure 5) is accessed to identify any other users within the community that have purchased the product.
  • the contact information for each such user is read from the table 86B or from the user database 82.
  • the contact information and associated base community names are incorporated into the product's detail page.
  • an option may additionally or alternatively be provided for the requester of the page to chat with any such other users that are currently online.
  • Figure 9 illustrates the off-line sequence of steps that are performed by the hotseller notifications process 80D.
  • the general purpose of this process is to identify, within each base community, any "hotselling" products (based on pre- specified criteria), and to call such products to the attention of those within the community that have not yet purchased the products.
  • the sequence 160-168 is performed once for each base community. In other implementations, the process could also be used to identify hotsellers in composite communities.
  • step 160 the process sequences through the products in the community's bestseller list while applying the hotseller criteria to each product. If multiple products qualify as hotsellers, only the "best" product is preferably selected. In one embodiment, a product is flagged as a hotseller if more than some threshold percentage (e.g., 5 %) of the community's members have recently purchased the product, as determined from the data within the community bestseller lists table 86A. This threshold could be a variable which depends upon the number of members of the community.
  • some threshold percentage e.g., 5 % of the community's members have recently purchased the product, as determined from the data within the community bestseller lists table 86A. This threshold could be a variable which depends upon the number of members of the community.
  • the position of the product within the community's bestseller list is compared to the product's position, if any, within the global community's bestseller list. For example, any title that is in one of the top ten positions within the community's list but which does not appear in the top 1000 bestsellers of the general population may automatically be flagged as a hotseller.
  • hotsellers may be identified by comparing the product's velocity or acceleration within the community to the product's velocity or acceleration within the global community.
  • the censored chi-square algorithm described in the attached appendix may be used to identify the hotsellers. In other implementations, these and other types of conditions or methods may be combined.
  • step 162 If no hotseller is found for the community (step 162), the process proceeds to the next base community (step 170), or terminates if all base communities have been processed. If a product is found, the product-to-member mapping table 86B ( Figure 5) is accessed to identify and obtain the contact information of any participating members that have purchased the product (step 164). In step 166, the process generates an email document or other notification message. As in Figure 4, this message preferably includes the contact information and a description of the product. In other implementations, the notifications may be communicated by facsimile, a customized Web page, or another communications method.
  • the notification message is sent by email to each base community member who both (1) has not purchased the product, and (2) has subscribed to the email notification service. Such members may be identified by conducting a search of the user database 82.
  • the notification messages could alternatively be sent out to all community members without regard to (1) and/or (2) above.
  • the contact information may be omitted from the notification message.
  • FIG 10 illustrates a sequence of steps that may be performed to implement the Purchase Notification service. This process may be implemented whenever a user completes the check-out process to purchase one or more products.
  • step 180 the user's profile is checked to identify any base communities in which the user participates in the Purchase Notification service. For each such community, all other participating members are identified in step 182.
  • step 184 a notification message is generated which includes a description of the purchased product(s) and the name of the common community. If the user participates in the Contact Information Exchange service, the contact information of the purchaser may also be included within this message.
  • the notification message is sent by email to all participating members identified in step 182. Alternatively, purchase notifications that have accumulated over a period of time may be displayed when a user logs into the system.
  • the various community-related features described above can also be implemented in the context of a network- based personal information management system.
  • One such system is implemented through the Web site of PlanetAII (www.planetall.com).
  • PlanetAII www.planetall.com
  • users can join various online communities and can selectively add members of such communities to a virtual, personal address book.
  • each user can selectively expose his or her own personal information to other community members on a user-by-user and datum-by-datum basis. Additional details of this system are described in U.S. appl. no. 08/962,997 titled NETWORKED PERSONAL CONTACT MANAGER filed November 2, 1997, the disclosure of which is hereby incorporated by reference.
  • the contacts listed within a user's address book may be treated as a "community" for purposes of implementing the above-described features.
  • a user may be given the option to view the products purchased by other users listed in his or her address book (or a particular section of the address book), or to view a bestsellers list for such users.
  • the contact information of other users within the address book that bought the same product may be displayed.
  • a user may be given the option to conduct a search of a friend's address book to locate another user that purchased a particular product.
  • the censored chi-square recommendation algorithm constructs a set of candidate recommendations for a predefined group of customers. It then conducts a statistical hypothesis test to decide whether or not these candidate recommendations are really a result of group preferences which differ from the preferences of the overall customer base. If the conclusion is that group preferences do differ significantly from overall customer preferences, the recommendations are presented to the group.
  • the inputs to the censored chi-square algorithm are the purchases made by the group (over some time period) and the purchases made by all customers (over the same time period).
  • Other types of events such as item viewing, downloading and rating events, can additionally or alternatively be used.
  • the purchases of the entire customer base are used to formulate expectations about how many customers in the group will have purchased each available item, given the total number of purchases by the group.
  • the "group purchase count" for each item is the number of customers in the group who actually purchased the item.
  • the candidate recommendations are first restricted to be those items whose group purchase counts exceeded expectations. Of these candidates, only those items with the largest group purchase counts are then retained. These final candidates are sorted according to how much their group purchase counts exceeded expectations (subject to a normalization). The values used to sort the candidates are called the "residuals".
  • I ⁇ i : there exists a c ⁇ in A such that ⁇ c, i > ⁇ in P ⁇
  • SJ1, S_(2), ..., S_(d) be the order statistics of S.
  • S_(d) is the number of distinct customers who purchased the most-purchased (positive- residual) item. Note ties are common, so that a subsequence S_(i), S_(i+ 1), ..., S i+j) may have all elements equal.
  • SR be the set of standardized residuals which correspond to elements of S that are > - S_ ⁇ .99 ⁇ .
  • the order statistic index of the final recommendation candidate is r* - max(m-r+ 1, 1).
  • the cX " 2 sampling distribution should converge asymptotically to the normal distribution as the number of observations over which the statistic is computed grows large.
  • the value of the cX " 2 can be shown to grow linearly in the total purchase count of the circle (community) as well as the number of items to recommend (i.e. terms in the cX " 2 summation). Since the purchase probabilities are constants under the null hypothesis, these are the only two variables with which the cX " 2 grows. So in theory we would want to bootstrap a distribution for each possible ⁇ n, r > pair, where n is the circle's purchase count and r the number of recommended items. In practice, both n and r are random variables which depend on the particular set of random customers we assemble at each iteration of the bootstrap.
  • the power of a test statistic (the probability the test statistic will detect deviations from the null hypothesis) is a nondecreasing function of the amount of data provided, so using all available data normally won't harm our statistical inferences.
  • the stationarity assumption behind the purchase probability estimates is at best only locally correct. The further back in time we look, the more likely it is that nonstationarity in the purchase probabilities will manifest itself in our hypothesis tests. Since this ⁇ onstationarity impacts the bootstrap as well, it is actually a pervasive problem that can't be circumvented with simple resampling, and it will tend to cause us to detect circle idiosyncrasies where none actually exist.
  • Determining the minimum lookback horizon consistent with this constraint would in general require repeated computations at successively longer horizons for a particular circle. Instead, for computational efficiency, we will forecast a horizon that has high probability of satisfying the constraint, accepting that in expectation some small percentage of circles will fail to satisfy it. The forecast is produced as a side effect of the bootstrap computation (see above). Each random group size we bootstrap over will have iterations at many horizons. At each horizon, some fraction of the iterations will fail the reasonability criterion. We record all such failures. Roughly speaking, the fraction of failures should decrease as lookback horizon increases.

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Abstract

Un système accessible sur Internet présente des éléments (80B-80E) de service d'informations qui permettent aux clients de choisir des produits ou d'autres types d'articles dans un catalogue électronique d'un commerçant. Les utilisateurs dudit système peuvent créer et se joindre à des communautés d'utilisateurs, telles que les communautés des loisirs, localités, professions et organisations. Le système accepte également des communautés de membres implicites fondées sur des adresses électroniques (par exemple tous les utilisateurs ayant l'adresse électronique 'nasa.com'), des adresses d'expédition/de facturation, et d'autres informations utilisateur connues. A l'aide de données d'historique d'achat réunies pour des utilisateurs en ligne, le système identifie automatiquement et génère des listes (86A) des articles les plus courants (et/ou des articles devenant courants) au sein d'une communauté donnée, et met ces informations à la disposition des utilisateurs pour visualisation.
PCT/US2000/040077 1999-04-09 2000-03-29 Systemes et procedes d'assistance aux utilisateurs pour choisir et evaluer des articles dans un catalogue electronique Ceased WO2000062223A1 (fr)

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US09/377,322 US7082407B1 (en) 1999-04-09 1999-08-19 Purchase notification service for assisting users in selecting items from an electronic catalog
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