Amazon Technologies, Inc.

Gregory D Linden

Jennifer A Jacobi

Eric A Benson

Brent Russell Smith

Nida K Zada

Russell Dicker

Jeffrey T Brownell

Joel R Spiegel

Ryan Justin Snodgrass

Michael D McDaniel

Various processes are disclosed for discovering item relationships between particular items, such as products represented in an electronic catalog, based on monitored user behaviors (e.g., item viewing activities, item purchases, shopping cart activities, etc.). The discovered item relationships may, for example, be used to generate personalized item recommendations for users, and/or to supplement item detail pages of an electronic catalog with lists of related items. Also disclosed are processes for generating personalized item recommendations based on users' search activities and browse node visits.

Automated detection and exposure of behavior-based relationships between browsable items

Recommendations based on items viewed during a current browsing session

Discovery of behavior-based item relationships

Discovery of behavior-based item relationships based on browsing session records

Improved user interfaces and methods are provided for presenting item recommendations to a user when the user selects an item to add to a shopping cart. In response to the user's selection, a page generation process generates and returns a page that includes a plurality of recommendations sections, each of which displays item recommendations generated using a different respective recommendation or selection algorithm (e.g., recommendations based on shopping cart contents, recommendations based on purchase history, etc.). These recommendation algorithms may be selected based on collected information regarding the user. The page may also include a shopping cart section which displays contents of the shopping cart.

User interface and methods for recommending items to users

Improved user interfaces and methods are provided for presenting item recommendations to a user when the user selects an item to add to an electronic shopping cart. In response to the user's selection, a page generation process generates and returns a page that includes a condensed shopping cart portion and a recommendations portion. The condensed shopping cart portion displays the contents of the shopping cart, but omits some of the item information and/or controls provided on a shopping cart page, thereby preserving more screen real estate for the display of item recommendations. The condensed shopping cart portion also includes a link to the shopping cart page.

A computer-implemented recommendation service uses item-to-item relationship mappings to select items to recommend to the user. The item-to-item relationship mappings may reflect user-behavior-based (e.g., purchase-based) item relationships, content-based item relationships, or a combination thereof. In one embodiment, personalized recommendations are generated for a user by a process that comprises retrieving from the mapping, for each of a plurality of items of interest to the user, a respective related items list; weighting the related items lists based on information regarding the user's affinity for the corresponding items of interest; combining the weighted related items lists to form a pool of scored items, and selecting items from the pool to recommend to the user.

Item recommendation service

A computer-implemented service analyzes purchase histories and/or other types of behavioral data of users on an aggregated basis to detect and quantify associations between particular items represented in an electronic catalog. The detected associations are stored in a mapping structure that maps items to related items, and is used to recommend items to users of the electronic catalog.

Computer processes for identifying related items and generating personalized item recommendations

Various methods are disclosed for monitoring user browsing activities, and for using such information to provide item recommendations to users. In one embodiment, a monitoring component of a merchant's Web site maintains a record of products viewed by each user during a current browsing session&#x2014;preferably based on visits to product detail pages. A recommendations component uses the resulting history of viewed products to identify additional products to recommend, preferably using a pre-existing table that maps products to related products. In one embodiment, this table is generated by periodically analyzing user browsing histories to identify correlations between purchases, viewing events, and/or other actions performed with respect to particular products. The recommended items may be displayed together with an option to individually deselect the recently viewed items on which the recommendations are based. Embodiments are also disclosed that use recent browse node visits, and recent searches, to generate the recommendations.

Content personalization based on actions performed during browsing sessions

A computer-implemented service analyzes purchase histories and/or other types of behavioral data of users on an aggregated basis to detect and quantify associations between particular items represented in an electronic catalog. The detected associations are stored in a mapping structure that maps items to related items, and is used to recommend items to users of the electronic catalog. The items may include products and/or categories of products.

Recommendation system

An improved user interface and method are provided for presenting recommendations to a user when the user adds an item to a shopping cart. In response to the shopping cart add event, a page generation process generates and returns a page that includes a recommendations portion and a condensed view of the shopping cart. The recommendations portion preferably includes multiple recommendation sections, each of which displays a different respective set of recommended items selected according to a different respective recommendation or selection algorithm (e.g., recommendations based on shopping cart contents, recommendations based on purchase history, etc.). The condensed shopping cart view preferably lacks controls for editing the shopping cart, and lacks certain types of product information, making more screen real estate available for the display of the recommendations content. A link to a full shopping cart page allows the user to edit the shopping cart and view expanded product descriptions.

Various methods are disclosed for monitoring user browsing activities that indicate user interests in particular products, or other items, represented in an electronic catalog, and for using such information to identify items that are related to one another. In one embodiment, relationships between items within an electronic catalog are determined by identifying items that are frequently viewed by users within the same browsing session (e.g., items A and B are related because a significant portion of those who viewed A also viewed B). The resulting item relatedness data may be stored in a table that maps items to sets of related items. The table may be used to provide personalized item recommendations to users, and/or to supplement item detail pages of the electronic catalog with lists of related items. In one embodiment, the table is used to provide session-specific item recommendations to users.

Data mining of user activity data to identify related items in an electronic catalog

A computer-implemented service recommends items to a user based on items previously selected by the user, such as items previously purchased, viewed, or placed in an electronic shopping cart by the user. The items may, for example, be products represented within a database of an online merchant. In one embodiment, the service generates the recommendations using a previously generated table that maps items to respective lists of 'similar' items. To generate the table, historical data indicative of users' affinities for particular items is processed periodically to identify correlations between item interests of users (e.g., items A and B are similar because a large portion of those who selected A also selected B). Personal recommendations are generated by accessing the table to identify items similar to those selected by the user. In one embodiment, items are recommended based on the current contents of a user's shopping cart.

Growing community of inventors

Seattle, WA, United States of America

Gregory D Linden