Amazon Technologies, Inc.

Jennifer A Jacobi

Eric A Benson

Gregory D Linden

Ryan Justin Snodgrass

Michael D McDaniel

Warren William Adams

Brian D Robertson

Kenneth L Dinovo

Jeffrey P Bezos

Russell Dicker

Jeffrey T Brownell

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.

Various computer-implemented services are disclosed for increasing the utility of electronic wish lists, and for assisting users in making gift purchases. One such service automatically notifies a user when an item being accessed in an electronic catalog is on, or is similar to an item on, an electronic wish list of an 'affiliated' user. The affiliations may be created explicitly by the users (e.g., by completing an online form), and/or implicitly based on actions performed by the users (e.g., when one user purchases a gift for another user). Another service automatically reminds a first user who is affiliated with a second user when a known or inferred gift-giving event of the second user is approaching. The reminder may include a link to a wish list of the second user, and/or may include or provide a link for viewing personalized gift recommendations for the second user.

Using inferred information about recurring gift giving events of users to provide messaging to affilated users

One embodiment of the present invention distributes data via a network to remotely located individuals. Evaluation feedback by one or more of the individuals is transmitted to others so that the feedback may be used by others to quickly locate the more useful data. The evaluation feedback may be in the form of ratings of product reviews. The feedback helps customers by allowing the more useful reviews can be quickly located.

Methods and systems for processing distributed feedback

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

Services for increasing the utility of electronic wish lists

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

One embodiment of the present invention distributes data via a network to remotely located users. Historical purchase information for a first user is stored in a database. Authorization is received over a network from the first user allowing a second user to view at least a portion of the historical purchase information. In addition, the first user selectively specifies which historical purchase information may be viewed by the second customer. An electronic notification is transmitted to the second user, wherein the notification informs the second user of the first user's authorization.

Methods and systems for distributing information within a dynamically defined community

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.

A facility for notifying a first user about a purchase made by a second user is described. The facility detects that the second user has purchased an item. The facility further determines that the first user has a purchase notification relationship with the second user. The facility then provides to the first user a notification that the second user has purchased the item.

Growing community of inventors

Seattle, WA, United States of America

Jennifer A Jacobi