US20150025996A1

US20150025996A1 - Systems and methods for recommending purchases

Info

Publication number: US20150025996A1
Application number: US14/321,864
Authority: US
Inventors: David Fishman; Srinivasa Boppana
Original assignee: PIPIT INTERACTIVE Inc
Current assignee: PIPIT INTERACTIVE Inc
Priority date: 2013-07-18
Filing date: 2014-07-02
Publication date: 2015-01-22
Also published as: US9928533B2; US20150026015A1

Abstract

A system and method for creating and sharing e-commerce inventory among a defined on-line community via a specialized visualization and interactivity interface is presented. One embodiment comprises a network system having a client-server architecture configured for exchanging data over a network. The data exchanges may pertain to various functions, such as on-line purchases, etc., and aspects, such as managing social networks, etc., associated with the network system. The network system may include a network-based marketplace, such as an e-commerce system, where traders or users may communicate and exchange data. A recommendation engine scrapes metadata from items selected in an “art-board” and extrapolates that data to recommend other items. These recommended other items should be based-on what other users have selected from the “art-board” and paired together, as well as what other users have removed from their respective art boards.

Description

The present application claims priority to Provisional Application Nos. 61/847,656 filed Jul. 18, 2013 and 61/847,890 filed Jul. 18, 2013.
The present invention is directed to a system and method for online shopping.
In recent years, with the popularization of the Internet and particularly of the World Wide Web, online shopping has revolutionized the retail industry. In contrast to brick-and-mortar malls, online shopping can be conducted from the privacy of the customer's home. In contrast to traditional catalog shopping, the buyer does not have to communicate the order to the retailer by mail, facsimile, or telephone; instead, the buyer can simply point and click to order. Also, since a human order taker does not have to read the order form or take the order over the telephone, the order can be fulfilled quickly and accurately.
However, online shopping also has the disadvantage that the buyer cannot physically inspect the item. While that disadvantage is minor for bookstores, it is a major problem for apparel retailers, since customers prefer to try on apparel before buying.
To overcome that disadvantage, various techniques for virtual modeling of apparel, particularly eyewear, have been developed. An illustrative example of such a technique is disclosed in U.S. Pat. No. 5,983,201 to Fay. The online retailer obtains digital images of the customer's head and face to obtain size and image data. Later, the customer can visit the online retailer's Web site from any location, such as the customer's home, to view various kinds of eyeglasses. The online retailer's server generates images of the customer with the eyeglasses resized to fit the customer's head to show how the customer would look in each kind of eyeglasses.
Apparel shopping is a social event. Many customers do not simply wish to see for themselves how they would look in a particular item of apparel; instead, they bring along friends or family members and solicit those friends' or family members' opinions before making a buying decision. Shoppers may also solicit the opinions of store clerks or of complete strangers. It is difficult to do any of those things in front of a computer. Furthermore, trips to brick-and-mortar shopping malls have a social role that online shopping has not yet duplicated.
It is also known in the art to allow potential buyers to exchange information about items over the Internet. Such information exchanges typically take the form of non-real-time message boards such as those on Deja.com, or the reader reviews of Amazon.com. The use of chat rooms to let potential buyers exchange information is taught by U.S. Pat. No. 6,041,311 to Chislenko et al, U.S. Pat. No. 6,049,777 to Sheena et al and U.S. Pat. No. 6,058,379 to Odom et al. However, such information exchanges do not overcome the above-noted problems with Fay and similar techniques. U.S. Pat. No. 6,901,379 discloses a system that allows a user to browse an online retailer's Web site or a mirror site and select an item and model the item online by having a server generate a digital image of the user wearing the item. If the user is still unsure as to whether to buy the item, the user can enter an online chat room in which the online modeling image is displayed to other users. The user can then receive the other users' feedback before deciding whether to buy the item. In a second embodiment, multiple online modeling images are generated to provide the user with a customized catalog, which can be of items for a single merchant or multiple merchants.
U.S. Pat. No. 7,949,659 discloses systems for selecting items to recommend to a user. The system includes a recommendation engine with a plurality of recommenders, and each recommender identifies a different type of reason for recommending items. In one embodiment, each recommender retrieves item preference data and generates candidate recommendations responsive to a subset of that data. The recommenders also score the candidate recommendations. In certain embodiments, a normalization engine normalizes the scores of the candidate recommendations provided by each recommender. A candidate selector selects at least a portion of the candidate recommendations based on the normalized scores to provide as recommendations to the user. The candidate selector also outputs the recommendations with associated reasons for recommending the items.
U.S. Pat. No. 8,170,919, issued to the assignee of the instant invention, discloses an inventive system and method for collaborative commerce that includes activating an art board, placing items onto the art board, inviting users to interact with the art board, and collaborating with the invited users. Additional features and functions include purchasing items shown on the art board, including by placing the items in a shopping cart, using e-mail, text messaging, and instant messaging to invite users, who may be chosen from a buddy list. Collaborating can be performed using voice chatting, video chatting, instant messaging, and text messaging, and includes examining reviews, ratings, reputations, and recommendations, and also includes displaying details regarding the items. In addition, reports comprising information regarding the items can be generated. A toolbar can be located on the art board and used to initiate inviting of users and placing of items onto the art board.

SUMMARY

A system and method for creating and sharing e-commerce product and/or content inventory among a defined on-line community via a specialized visualization and interactivity interface is presented. One embodiment comprises a network system having a client-server/Web services architecture configured for exchanging data over a network. The data exchanges may pertain to various functions, such as on-line purchases, etc., and aspects, such as managing social networks, etc., associated with the network system. The network system may include a network-based marketplace, such as an e-commerce system, where traders or users may create, consume, communicate and exchange data. The Recommendation engine consumes the attributes from a website, specific section of the site, channel, product, content, and visitors behavioral data including but not limited to Geographic, navigational, website, product, content, search keywords (onsite or offsite) attributes and then recommend art boards or collections based on one or more of those attributes.
Recommendation system can be available on any website by adding the webservices logic to communicate with the recommendation engine. Recommendation modules can displayed as a Display Ad module on external web and mobile sites/apps. Recommendation engine can be accessible on any device that can communicate via web or internet that can communicate via web services.
A recommendation engine scrapes metadata from items selected in an “art-board” and extrapolates that data to recommend other items. These recommended other items should be based-on what other users have selected from the “art-board” and paired together, as well as what other users have removed from their respective art boards. A recommendation engine also scrapes metadata

- a. from art boards and extrapolates that data to recommend other art boards. These recommended art boards may be based-on what other users have liked, viewed, shared from the “art-board” and/or paired together, as well as what other users have removed from their respective likes or shares.
- b. from a given product attribute(s) (for e.g., brand name, or product category, or price, etc.,), visitor behavioral attribute(s) (colors, sections of a website, or geographic information), content attribute(s) (names, titles, etc.,) and/or associated attributes (search keywords, etc.,) and recommend art boards that are relavant or similar, or provide alterantive art boards.

In one implementation, a web-based product provides ways to access the recommendation engine services using web services and leveraging a user interface that's written in HTML5/web/native mobile app, which can be both a web-based storefront (with the recommendation engine and transactional capabilities built-in), as well as a mobile rich-media ad unit with very similar functionality of the full version of the product (the web-based store front), except in a smaller scale with more targeted options (based on the demographic the mobile ad is displayed-to). This product will function as a personal shopping recommendation engine, and will, at some point, also be integrated in the brick-and-mortar/physical retail shopping experience
In one embodiment, the system can recommend products that others with similar preferences have “liked” or “disliked,” allowing users to create custom curated collections that are influenced by the process (essentially, influenced by other users of the system as well as the system's pairing for tags included in the metadata).
The present invention advantageously provides a system and method for aggregating product information onto a visualization board from products initially resident on a network-based marketplace, such as a website, or a mobile app or an e-commerce web site. The products and their aggregated product information can be displayed on the visualization board in a manner enabling multiple users to interact and view groups of products from single or multiple websites, while the users are engaging, chatting and interacting on-line with each other and, if desired, traversing among web sites.
The inventive system and method comprises activating an art board, automatically recommending items onto the art board from a user's recently viewed items palette, inviting users to interact with the art board and other users' recently viewed items palettes, and collaborating with the invited users. An art board can be activated whereas user's recently viewed items can be made public to any networked user of the system. Additional features and functions include purchasing items shown on the art board, including by placing the items in a shopping cart, using e-mail, text messaging, and instant messaging to invite users, who may be chosen from a buddy list. Collaborating can be performed using voice chatting, video chatting, instant messaging, and text messaging, and includes examining reviews, ratings, reputations, and recommendations, and also includes displaying details regarding the items. In addition, reports comprising information regarding the items can be generated. A toolbar can be located on the art board and used to initiate inviting of users, creation of new art boards, and copy and/or save of art boards. The recently viewed items palette can be located on the art board and shared with other users of the system based on permissions. The system enables creating and sharing of product, content, or e-commerce inventory among a defined on-line community via a specialized visualization and interactivity interface. One embodiment comprises a network system having a webservices / client-server architecture configured for exchanging data over a network. The data exchanges may pertain to various functions, such as on-line purchases, etc., and aspects, such as managing social networks, etc., associated with the network system. The network system may include a network-based marketplace, such as an e-commerce system, where traders or users may communicate and exchange data.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention is further described in the detailed description that follows, by reference to the noted drawings by way of non-limiting illustrative embodiments of the invention, in which like reference numerals represent similar parts throughout the drawings. As should be understood, however, the invention is not limited to the precise arrangements and instrumentalities shown. In the drawings:

FIG. 1 shows an exemplary process for recommending items on an interactive art-board.

FIG. 2 shows an exemplary art-board system for creating and sharing e-commerce inventory among a defined on-line community through a specialized visualization and interactive interface.

FIG. 3 shows an exemplary recommended Art-Boards for a Spring Shopper (visitor behavior) or based on a “Fashion” keyword.

FIG. 4 shows a mockup of potential use of “Recommendation” module consuming the visitors data (in this case “Style”).

DETAILED DESCRIPTIONS

FIG. 1 shows a flow diagram of an exemplary method of the invention. In step S1, a user or host activates an art board 10 (FIG. 2) from a network-based marketplace 36. In step S2, one or more items are placed onto the art board 10. In step S3, a recommendation engine is used to recommend additional items to place on the art board 10. Next, in S4, the host invites other users to interact with the art board 10. This invitation can be made via e-mail, SMS, IM or other electronic means. In step S5, users collaborate regarding art board items. This collaboration can include chatting, instant messaging, adding items to the art board, etc. In step S6, one or more users, including the host, can purchase items shown on the art board 10. In one embodiment, items are purchased by moving them to an electronic shopping cart, paying electronically, and having the items delivered to the purchaser. Optional step S7 can produce reports, such as statistical data relating to products and/or user preferences in accordance with the items on the art board.
Steps S2 through S6 can be performed in any order and each step can be performed more than one time. For example, the host can invite two friends, step S4, the three users can collaborate, step S5, one or more of the users can place one or more items onto the art board, step S3, a user can purchase an item S6, etc.
In one implementation, the process includes activating an art board, automatically recommending and placing items onto the art board, inviting users to interact with the art board, and collaborating with the invited users. Additional features and functions include purchasing items shown on the art board, including by placing the items in a shopping cart, using e-mail, text messaging, and instant messaging to invite users, who may be chosen from a buddy list. Collaborating can be performed using voice chatting, video chatting, instant messaging, and text messaging, and includes examining reviews, ratings, reputations, and recommendations, and also includes displaying details regarding the items. In addition, reports comprising information regarding the items can be generated. A toolbar can be located on the art board and used to initiate inviting of users and placing of items onto the art board.
Next, the recommendation engine operation is discussed. In one embodiment, a web-based product with a user interface that's written in HTML5, which can be both a web-based storefront (with the recommendation engine and transactional capabilities built-in), as well as a mobile rich-media ad unit with very similar functionality of the full version of the product (the web-based store front), except in a smaller scale with more targeted options (based on the demographic the mobile ad is displayed-to). This product will function as a personal shopping recommendation engine, and will, at some point, also be integrated in the brick-and-mortar/physical retail shopping experience.
The recommendation engine scrapes metadata from items selected in a “art-board” and extrapolates that data to recommend other items. These recommended other items should be based-on what other users have selected from the “art-board” and paired together, as well as what other users have removed from their respective art boards.
Essentially, this process is analogous to Pandora's music genome project, recommending music that others with similar preferences have “liked” or “disliked,” allowing users to create custom curated collections that are influenced by the process (essentially, influenced by other users of the system as well as the system's pairing for tags included in the metadata).
The recommendations processes operate by attempting to match users to other users having similar behaviors or interests. For example, once Users A and B have been matched, items favorably sampled by User A but not yet sampled by User B may be recommended to User B. In contrast, content-based recommendation systems seek to identify items having content (e.g., text) that is similar to the content of items selected by the user. Other recommendation systems use item-to-item similarity mappings to generate the personalized recommendations. The item-to-item mappings may be generated periodically based on computer-detected correlations between the item purchases, item viewing events, or other types of item selection actions of a population of users. Once generated, a dataset of item-to-item mappings may be used to identify and recommend items similar to those already “known” to be of interest to the user. In one embodiment, the system can be built by:

- 1. Writing a process (or set of processes) that conforms-to (utilizes) the Purchlive system backend (with its tags/categorization, as well as content population feature).
- 2. Building a new backend with a similar feature-set to the current Purchlive system (namely, one that has tags/categorization, as well as content population feature) that integrates an process (or set of process) that accomplish the purpose (as listed in the first paragraph of this document).

When building the recommendation engine, several factors must be taken into account. The engine can consider factors such as:
Data Available for process
Real time vs. Aggregated recommendations
Weighting suggestions (process definition)
External data usage
Third party recommendation engines
One embodiment uses available data such as, but not limited to:
Product Side Data—uploaded into the system by customer

- Title
- Description
- Current Price (or Sale Price)
- MSRP (Non-Sale Price)
- Keywords/tags

App Data—produced when users take action

- Products Viewed
- Products Added to Artboard
- Products Saved in Artboard
- Products Shared within Artboard
- Products Commented upon (Must integrate with FB)

Visit Data—this data can be obtained from Google Analytics

- Customers viewing, adding, saving, sharing products
- Interactions per Product
- Overall Interactions
- (not real-time) Geographic Location

Product Sales Data—Customer Side

- Orders
- Sales
- Revenue
- Cost of Goods

In an embodiment, the recommender analyzes a subset of the item preference data to identify items as candidate recommendations for recommending to a user. Each recommender also identifies one or more reasons for recommending the items. As discussed below, different recommenders may use different types of item preference data than others to select candidate items to recommend. Different recommenders may also provide different types of reasons for recommending items.
For example, a particular recommender might retrieve the user's purchase history data. Using this data, the recommender can find items owned by the user that are part of a series. A series might include, for instance, books in a trilogy, movies and their sequels, or all albums by a musician. If the user has purchased fewer than all the items in the series, the recommender might select the remaining items as candidate recommendations and provide a reason such as, “this item is recommended because you purchased items A and B, and this item would complete your series.” Advantageously, this reason can be more compelling than a reason such as “because you purchased items A and B, and this item is similar.” Users may therefore be more inclined to trust the reasons provided by the recommenders.
As another example, a recommender might obtain data about a user's friends. This friends data might include information on the friends' birthdays, their wish lists, and their purchase histories. Using this data, a recommender might suggest gifts that could be bought for a friend's upcoming birthday and provide a reason such as “this item is recommended because your friend John's birthday is on July 5th, and this item is on his wish list.” Provided with such a reason, the user might be more inclined to buy the item.
Many other examples of item preference data may be used by the recommenders to generate candidate recommendations and corresponding reasons. For instance, browse history data (e.g., data on user searches, clicks, and the like) may be used to provide a recommendation with the reason, “because this item is similar to an item you searched for.” Purchase history data and/or wish list data might be used to provide a recommendation with the reason, “because this item might be interesting to an early adopter such as you.” Browse history data on a browse node of interest to the user (e.g., a category browsed by the user) might be used to provide a recommendation with the reason, “because this item is a top seller in one of your favorite interest areas.” Various other forms of item preference data may be used to provide recommendations with reasons such as “because you recently moved,” “because you bought an item that may need replacing,” “because most people upgrade their shoes after two years,” or the like.
Multiple reasons may be provided by a single recommender, or multiple recommenders may each provide the same candidate recommendation along with a different reason for that recommendation. For instance, several recommenders may be used to recommend a particular pant because 1) a user recently rated several pants, 2) this is the best-selling movie in the pant category, and 3) this pant was nominated for two awards. Using multiple reasons may provide further motivation to the user to view or buy an item.
The system can perform real time as well as aggregated recommendations. When building the recommendation engine there will be data that can be mined in real-time to provide relevant results. These real-time metrics will be constantly available based upon user patterns. Other metrics will come from outside sources and may be combined into the process on a batch process. The system can include a mobile-ad unit which can perform collecting real-time metrics within ads.
With limited data at the start, the recommendations and the data needed to make good ones may take some time. In order to assure that products start to get an even chance at rankings (products towards back of stack browser may never get the right chance), we should start to randomize the display of products within the creation process. Currently the same shoes, skirts, etc. are being displayed up front which will skew results.
In one embodiment of a Real-time Weighted process, due to the lack of data upon launch, a linear process will be built that simply provides point values to products. This will determine both placement of products and order in which collections are displayed to users. As data becomes more available to each product and its related entities a stronger and more sloped process can be utilized.
Next, Product Weighting can be done. Each Product will record the following numeric values any time a product is interacted with. The higher the score, the higher the chances that products will be displayed to users. This weighting happens pre-user activity. Each product view will also be recorded, and divided into the current weighting scale. This will allow newer products introduced to still grow relevance. For example, the product can be scored as follows:
1 pt (simple interaction)
3 pt (adding interaction)
5 pt (sharing interaction)
10 pt (buying interaction)
The system then performs Product Relationship weighting. In one embodiment, a set of tables will be managed recording each product and how it was used with other products. This will be summed and influence the display of products after a user starts to show interest.
1 pt (added Product A with Product B)
2 pt (user has selected Product A and Product B but not in same outfit)
3 pt (outfit saved with Product A and Product B)
5 pt (outfit shared with Product A and Product B)
Collection Relationship weighting is performed next in one embodiment. This additional weighting will be done for collections that are very similar to the Product weighting scale used above. This weighting occurs pre and post user interaction. Pre post allows for proper collection display and ranking while post use interaction weights can be used to provide “other interested” looks or collections. As with Product weights, the sum of the values will be divided by the number of impressions a collection receives.
1 pt (browsed collection)
3 pt (shopped collection)
5 pt (edited collection)
Third Party Recommendation Engines may be applied to the process. Once such engine is Brilliance, an affordable solution with little integration needs. With over 20 recommendations including products bought after matches, customers also liked, bundled products, and more. Another engine is Strands—with a very involved integration. The system can incorporate Strands in many areas and take advantage of reviews and ratings from the system users, and there is also a iPhone SDK available to incorporate recommendations into an app. Strands allows for several built-in as well as configurable recommendation types. Yet another recommendation engine is Certona, a popular of all recommendation engines, as well as the most robust and customized. Rich relevance—{rr} is also used on some of the internee's largest sites including O.com, WalMart and Sears.
In one implementation, the recommender engine can:

- categorize customer tags (summer shoes), comments from art board
- categorize likes/dislikes/ratings/sharing option
- categorize based on the art board item's parent product attributes, including product names, categories, manufacturers, color, size=material, price, sale price, campaign and other attributes from product catalog
  Ad/targeting options (Able to present ad units/runway)

Recommendation engine

- Merchandising override rules with predefined list of items based on
  - specific category
    - specific campaign
    - attributes (based on price, sale price, inventory, campaign, color, brand, popularity, size);—
      Dynamic—based on

Also liked

- Also viewed
- Tags
- popular for each category
- product attributes

Publishers:

- capture the site URL, page meta data where the art board is created, accessed Analytics can perform the following:
  Track site information for e.g., where the art board is created, and other site attribute=(portal, social media, blogs, etc.,)
  Track technology information
  Track interactions (click, slide, add, remove, share=like, save, views)

FIG. 2 shows a schematic illustration of an exemplary embodiment of the present invention. As shown in FIG. 2, when a visualization board 10 is activated, a new user interface layer is created above the normal user interface screens associated with a network-based marketplace 36. This visualization layer visually appears as a floating window above the normal web interface associated with the network-based marketplace 36. In one embodiment, FLEX Data Services from Adobe Corporation can be used to support the presentation layer of the visualization board 10 and to provide ways to send and load data to and from server-side components without requiring the client to reload the view of the underlying network application layers. In one embodiment, the visualization board 10 is PurchLive, available at www.purchlive.com. PurchLive is an advanced user-generated content engine, combined with a powerful analytics engine, that allows a customer to use existing content in new ways and generates fresh insights into consumer behavior. By adding a new dimension to the web site, PurchLive leverages the user's existing investment and creates a valuable new viral marketing channel.
FIG. 3 shows an exemplary recommended Art-Boards for a Spring Shopper (visitor behavior) or based on a “Fashion” keyword, while FIG. 4 shows a mockup of potential use of “Recommendation” module consuming the visitors data (in this case “Style”). From a given product attribute(s) (for e.g., brand name, or product category, or price, etc.,), visitor behavioral attribute(s) (colors, sections of a website, or geographic information), content attribute(s) (names, titles, etc.,) and/or associated attributes (search keywords, etc.,) and recommend art boards that are relevant or similar, or provide alternative art boards. As shown in FIG. 3, another application of the recommendation engine is shown. The system can show recommended art boards based on a given product or a product category page attributes or other attributes (say search keyword attributes). In this embodiment, the art board will dynamically change based on the product/content/other attributes the user are providing to the recommendation engine. For example if the user is on a “Style” article page, the “recommendation” module will show dynamically/real-time relevant artboards (outfits) that match with “Style” or “fashion” or related Art boards.
In one embodiment, the recommender engine automatically suggests items available for purchase that best matches the user's interest and places these items onto the visualization board 10. Other users can also place items onto the visualization board 10. At all times, all users see the same visualization board 10, including users invited into the session after its initialization. Any product added or removed, or any Cartesian movement of products on the visualization board 10, is automatically synchronized to the visualization boards seen by other users. This is because any manipulation of the visualization board 10 by any one user is automatically annotated into a server, e.g. the network-based provider, that synchronizes the visualization board 10 of all other users members of that session. This also applies to other data, such as annotations, product information and metadata, that any user could add.
The visualization board 10 combined with the network and marketplace applications can include one or more applications which support the network-based marketplace, and can generate and maintain relationships between products, community groups and their members' rules and roles, and transactions that may be associated with the network-based marketplace shopping cart including the products purchased through it. The associated relationships may include distribution parameters, e.g., roles and rules pertaining to the item list and associated community group(s), reviews and recommendations pertaining to the items of the item list, item attributes like model and manufacturer, or service provider of a particular item, item status, e.g., purchased, etc. Additionally, the various applications may support social networking functions, including building and maintaining the community groups created by a user, relating one or more item lists to selected community groups, and providing a shared electronic shopping cart for the community groups to purchase items from the shared item list.
On-line store or e-commerce applications may allow sellers to group their listings, e.g., goods and/or services, in the visualization boards 10 within a “virtual” store, which may be branded and otherwise personalized by and for the sellers. Such virtual storyboards 10 may also offer promotions, incentives and features that are specific and personalized to a relevant seller. In one embodiment, the listings and/or transactions associated with the virtual storyboards and their features may be provided to one or more community groups having an existing relationship with the item list creator. An existing relationship or association may include a friend or family relationship, a transactional relationship, e.g., prior sales with user, or an overall network community relationship, e.g., buyers historical transaction rating. Reputation applications may allow parties that transact utilizing the network-based marketplace 36 and the storyboards 10 to establish, build and maintain reputations, which may be made available and published to potential trading partners.
A number of fraud prevention applications may implement various fraud detection and prevention mechanisms to reduce the occurrence of fraud within the marketplace. In one embodiment, the fraud prevention applications may monitor activities of each user within the community group. For example, the item list creator may want to be informed if a member of the community group adds items to the virtual storyboard or changes shipping information, provided the member had the necessary permissions. In various embodiments, whether to monitor and the level of monitoring may depend upon the relationship to the item list creator. For example, an indirect relationship may be more heavily monitored than a direct relationship.
Messaging applications may be used for the generation and delivery of messages to users of the network-based marketplace 36. Messages can, for example, advise the visualization board creator and members of the community groups associated with an item list of the status of the various items on the list, e.g., already purchased, etc. In one embodiment, the messaging applications may be used in conjunction with the social networking applications to provide promotional and/or marketing information to the community members associated with the item list to assist them in finding and purchasing items on the visualization board 10.
A reporting application connected with the virtual storyboard 10 can compile statistical data relating to the products, selection, choices, and/or preferences of users with respect to selecting products and/or combinations. A ranking system could be created whereby such information is compiled statistically and made available to merchants for trend analysis. Additionally such information could be combined with “recommendation engines” to suggest products automatically or manually. In one embodiment, such recommendation engine could include a collaborative filtering engine that catalogs and indexes similar users with their choices of products and recommends the choices of one similar user to the others.
A user table may contain a record for each registered user of the network-based marketplace, and may include identifier, address and financial instrument information pertaining to each such registered user. In one embodiment, a user operates as an item visualization board creator or a member of a community group, including associated operations pertaining to the rules and roles, created by the visualization board creator. A user may also operate as a seller, a buyer, or both, within the network-based marketplace. The tables may also include a visualization board table that maintains listing or item records for goods and/or services created by a visualization board creator. In one embodiment, the visualization board is created for sharing with a community group defined, at least in part, by the visualization board creator.
Furthermore, each listing or item record within the visualization board table may be linked to one or more electronic shopping cart records within a electronic shopping cart table and to one or more user records within the user table and/or a vendor table, to associate a seller or vendor and one or more actual or potential buyers from the community group with each visualization board.
A transaction table may contain a record for each transaction pertaining to items or listings for which the user defined community group rules and roles pertain to one or more items of the visualization board. For example, the visualization board creator may not want a member of a community group to be able to view, purchase, edit, etc., any or all of the items in the visualization board. In another example, rules may include an ability to purchase an item on the list, purchase one or more items using the creator's account, add to the visualization board, etc.
Additionally, the visualization board creator may want to assign roles to an entity within the community group. For example, roles may include a buyer, a reviewer, an administrator, etc. Accordingly, a rules applications and a roles applications may be used in conjunction with social networking applications to customize the visualization board to be shared within one or more community groups.
The relationship or association between the visualization board creator (user) and the members of the one or more community groups may be a direct relationship or an indirect relationship. An example of a direct relationship may be a sister, a friend, or a trusted associate user, while the indirect relationship may be a secondary entity brought in by a direct relationship. The web servers can access one or more additional repositories of user data. Because a group of individuals can share an account, a given “user” may include multiple individuals (e.g., two family members that share a computer). The data stored for each user may include one or more of the following types of information (among other things) that can be used to generate recommendations in accordance with the engine: (a) the user's purchase history, including dates of purchase, (b) a history of items recently viewed by the user, (c) the user's item ratings profile, if any, and (d) items tagged by the user. Various other types of user information, such as wish list/registry contents, email addresses, shipping addresses, shopping cart contents, and browse (e.g., clickstream) histories, may additionally be stored.
The network system also includes a network-based provider having a data exchange platform, such as an art board, to provide server-side functionality via a network, e.g., the
Internet, to one or more clients, including users that may utilize the network system through the network-based provider to exchange data over the network. The data exchange may include transactions such as receiving and processing data from a multitude of users. The data may include, but is not limited to, shared recently viewed products, product and service reviews, product, service, manufacture, and vendor recommendations, product and service listings, auction bids, feedback, etc.
In an exemplary embodiment, the network-based marketplace, the network-based provider including the data exchange platform, an application program interface (API) server, and a web server are coupled to, and provide programmatic and web interfaces respectively to, one or more application servers. The application servers host one or more networking applications and marketplace applications. The applications servers, in turn, are coupled to one or more database servers that facilitate access to one or more databases. The marketplace application may provide a number of marketplace functions and services, e.g., listing, payment, etc., to users that access the network-based marketplace.
This inventive system also embodies the notion of a third party application, executing on a third party server machine, as having programmatic access to the network-based marketplace via the programmatic interface provided by the API server. For example, the third party application may, utilizing information retrieved from the network-based marketplace, support one or more features or functions on a website hosted by the third party. The third party website may, for example, provide one or more networking, marketplace or payment functions that are supported by the relevant applications of the network-based marketplace. Under such embodiments, multiple network and marketplace applications, respectively, could be part of the network-based marketplace.
Various other applications, separate or as part of the network-based marketplace, may support social networking functions. These could include allowing the user to create groups of other users, affiliates, and lists of friends, and to facilitate various group communications to those lists and users, including distributing products in the network-based marketplace. While the social networking applications and the marketplace applications are discussed here as joined to form part of the network-based marketplace, in alternative embodiments, the networking applications may form part of a social networking service that is separate and distinct from the marketplace.
The various components of the web site system may run, for example, on one or more servers (not shown). In one embodiment, various components in or communicating with the recommendations service are replicated across multiple machines to accommodate heavy loads.
Each of the processes and algorithms described above may be embodied in, and fully automated by, code modules executed by one or more computers or computer processors. The code modules may be stored on any type of computer-readable medium or computer storage device. The processes and algorithms may also be implemented partially or wholly in application-specific circuitry. The results of the disclosed processes and process steps may be stored, persistently or otherwise, in any type of computer storage.
The various features and processes described above may be used independently of one another, or may be combined in various ways. All possible combinations and subcombinations are intended to fall within the scope of this disclosure. In addition, certain method or process steps may be omitted in some implementations.
Although this disclosure has been described in terms of certain example embodiments and applications, other embodiments and applications that are apparent to those of ordinary skill in the art, including embodiments and applications that do not provide all of the benefits described herein, are also within the scope of this disclosure. The scope of the inventions is defined only by the claims, which are intended to be construed without reference to any definitions that may be explicitly or implicitly included in any of the incorporated-by-reference materials.

Claims

What is claimed is:

1. A method for performing e-commerce, comprising:

activating an art board from a network-based marketplace;

placing one or more items onto the art board;

recommending additional items to place on the art board;

inviting users to interact with the art board; and

collaborating regarding art board items.

2. The method of claim 1, comprising inviting users selected from a buddy list.

3. The method of claim 1, wherein the collaborating comprises voice chatting, video chatting, instant messaging, or text messaging.

4. The method of claim 1, comprising examining reviews, ratings, reputations, and recommendations.

5. The method of claim 1, comprising displaying a toolbar on the art board and used to inviting of users and placing of items onto the art board.

6. The method of claim 1, comprising generating a user interface written in HTML5 for a web-based storefront with a recommendation engine and transactional capabilities built-in), as well as a mobile rich-media ad unit with very similar functionality of the full version of the product (the web-based store front), except in a smaller scale with more targeted options

7. The method of claim 1, comprising scraping metadata from items selected in the art-board and extrapolating data to recommend other items.

8. The method of claim 1, wherein the recommended other items are based-on what other users have selected from the art-board and paired together, and what other users have removed from their respective art boards.

9. The method of claim 1, comprising recommending a similar art-board or an alternative art-board based on product attribute(s), visitor behavioral attribute(s), content attribute(s), associated attributes.

10. The method of claim 1, comprising performing real time and aggregated recommendations, building the recommendation engine by mining data in real-time to provide relevant results and generating real-time metrics based upon user patterns.

11. A network system, comprising:

a processor;

a database coupled to the processor; and

a data storage device coupled to the processor containing code for:

activating an art board from a network-based marketplace;

placing one or more items onto the art board;

recommending additional items to place on the art board;

inviting users to interact with the art board; and

collaborating regarding art board items.

12. The system of claim 11, comprising code for inviting users selected from a buddy list.

13. The system of claim 11, wherein the collaborating comprises voice chatting, video chatting, instant messaging, or text messaging.

14. The system of claim 11, comprising code for examining reviews, ratings, reputations, and recommendations.

15. The system of claim 11, comprising code for displaying a toolbar on the art board and used to inviting of users and placing of items onto the art board.

16. The system of claim 11, comprising code for generating a user interface written in HTML5 for a web-based storefront with a recommendation engine and transactional capabilities built-in), as well as a mobile rich-media ad unit with very similar functionality of the full version of the product (the web-based store front), except in a smaller scale with more targeted options

17. The system of claim 11, comprising code for scraping metadata from items selected in the art-board and extrapolating data to recommend other items.

18. The system of claim 11, wherein the recommended other items are based-on what other users have selected from the art-board and paired together, and what other users have removed from their respective art boards.

19. The system of claim 11, comprising code for recommending a similar art-board or an alternative art-board based on product attribute(s), visitor behavioral attribute(s), content attribute(s), associated attributes.

20. The system of claim 11, comprising code for performing real time and aggregated recommendations, building the recommendation engine by mining data in real-time to provide relevant results and generating real-time metrics based upon user patterns.