Use of electronic shopping carts to generate personal recommendations

In a computer system that provides user access to a database of items, and provides electronic shopping carts for allowing users to collect and hold items for prospective purchase, a method of predicting items that are of current interest to a user, comprising the computer-implemented steps of:

• (a) identifying a plurality of items that are currently in the user's shopping cart;
• (b) for each item identified in step (a), accessing a non-user-specific data structure which maps items to similar items to identify a corresponding set of similar items, to thereby identify multiple sets of similar items;
• (c) combining the multiple sets of similar items to form a ranked set of similar items in which an item's ranking reflects whether that item appears within more than one of the multiple sets;
• and
• (d) recommending at least some of the items of the ranked set of similar items to the user.

In a computer system that provides user access to a database of items, and provides electronic shopping carts for allowing users to collect and hold items for prospective purchase, a computer-implemented method of recommending items to a user, comprising:

• (a) providing a non-user-specific data structure which indicates similarities between items of the database;
• (b) identifying a first plurality of items that are of current interest to the user, the first plurality of items including at least two items that are currently in an electronic shopping cart of the user;
• (c) using the data structure to identify a second plurality of items that are similar to one or more of the first plurality of items and
• (d) selecting, from the second plurality of items, a subset of items to recommend to the user, wherein an item is selected based in part on whether it is similar to more than one of the first plurality of items.

In a computer system that provides user access to a database of items that are available for purchase, and which allows users to create and use multiple shopping carts within a common account with a merchant to hold items for prospective purchase, a method of recommending items that correspond to a particular shopping cart of a user that has multiple shopping carts, comprising:

• (a) providing a non-user-specific data structure which indicates similarities between items of the database;
• (b) identifying a shopping cart of the plurality of shopping carts of the user;
• (c) identifying a first plurality of items that have been placed by the user into the shopping cart identified in step (b);
• (d) using the data structure to identify a second plurality of items that are similar to one or more of the first plurality of items;
• and
• (e) selecting, from the second plurality of items, a subset of items to recommend to the user, wherein an item is selected based in part on whether that item is similar to more than one of the first plurality of items.

A computer-implemented method of recommending items to a user, comprising:

• identifying a plurality of items that are currently in the user's shopping cart;
• using the plurality of items in the user's shopping cart to generate a list of additional items that are predicted to be of interest to the user, wherein an additional item is selected for inclusion in the list based in-part upon whether that additional item is similar to more than one of the plurality of items in the user's shopping cart;
• and
• displaying the list of additional items to the user when the user views contents of the shopping cart.

A method of recommending products to a user, comprising:

• generating a data structure which maps individual products to sets of related products in which product relatedness is determined based at least in-part on an automated analysis of user purchase histories of products;
• identifying a plurality of products that are currently in a shopping cart of a user;
• for each of the plurality of products, accessing the data structure to identify a corresponding set of related products, to thereby identify a plurality of sets of related products;
• and
• selecting related products from the plurality of sets to recommend to the user based in part on whether a related product falls within more than one of said sets, such that products that are related to more than one of the products in the user's shopping cart tend to be recommended to the user over products related to only a single product in the shopping cart.