Amazon Technologies, Inc.

Nida K Zada

Brent Russell Smith

Gregory D Linden

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

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

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

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

Use of product viewing histories of users to identify related products

Various methods are disclosed for monitoring user browsing activities, and for using such information to provide session-specific 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.

Growing community of inventors

Seattle, WA, United States of America

Nida K Zada