Amazon Technologies, Inc.

Eric A Benson

Jennifer A Jacobi

Gregory D Linden

Russell Dicker

Jeffrey T Brownell

Paul G Nordstrom

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 system and methods for dynamically generating electronic documents, such as web pages or XML documents, provides increased program parallelism and reduced document generation times. In one embodiment, two separate threads are used: a first thread that processes a document template, and a second thread that handles requests for data. Also disclosed are methods for selectively combining particular data requests for transmission on a network.

Dynamic generation of documents

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

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

A system and process are disclosed for encoding selected data structures in browser cookies. The data structures may contain a variety of different types of data elements, including N-bit integers and other non-character elements. A version tracking scheme provides forward and backward compatibility between client and server software. The process is implemented without the need for any browser extensions, and without the need for users to download any special code to their computers.

Use of browser cookies to store structured data

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 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.

Personalized recommendations of items represented within a database

A web site system implements a process for storing selected data structures within browser cookies. The data structures may contain a variety of different types of data elements, including N-bit integers and other non-character elements. A version tracking scheme provides forward and backward compatibility between client and server software. The process is implemented without the need for any browser extensions, and without the need for users to download any special code to their computers.

A computer-implemented service recommends products or other items to a user based on a set of items known to be of interest to the user, such as a set of items currently in the user's electronic shopping cart. In one embodiment, the service identifies items that are currently in the user's shopping cart, and uses these items to generate a list of additional items that are predicted to be of interest to the user, wherein an additional item is selected to include in the list based in-part upon whether that item is related to more than one of the items in the user's shopping cart. The item relationships are preferably determined by an off-line process that analyzes user purchase histories to identify correlations between item purchases. The additional items are preferably displayed to the user when the user views the contents of the shopping cart.

Use of electronic shopping carts to generate personal recommendations

A recommendations service recommends items to individual users based on a set of items that are known to be of interest to the user, such as a set of items previously purchased by the user. In the disclosed embodiments, the service is used to recommend products to users of a merchant's Web site. The service generates the recommendations using a previously-generated table which maps items to lists of &ldquo;similar&rdquo; items. The similarities reflected by the table are based on the collective interests of the community of users. For example, in one embodiment, the similarities are based on correlations between the purchases of items by users (e.g., items A and B are similar because a relatively large portion of the users that purchased item A also bought item B). The table also includes scores which indicate degrees of similarity between individual items. To generate personal recommendations, the service retrieves from the table the similar items lists corresponding to the items known to be of interest to the user. These similar items lists are appropriately combined into a single list, which is then sorted (based on combined similarity scores) and filtered to generate a list of recommended items. Also disclosed are various methods for using the current and/or past contents of a user's electronic shopping cart to generate recommendations. In one embodiment, the user can create multiple shopping carts, and can use the recommendation service to obtain recommendations that are specific to a designated shopping cart. In another embodiment, the recommendations are generated based on the current contents of a user's shopping cart, so that the recommendations tend to correspond to the current shopping task being performed by the user.

Growing community of inventors

Seattle, WA, United States of America

Eric A Benson