Mining of user event data to identify users with common interests

摘要

A computer-implemented matching service matches users to other users, and/or to user communities, based at least in part on a computer analysis of event data reflective of user behaviors. The event data may, for example, evidence user affinities for particular items represented in an electronic catalog, such as book titles, music titles, movie titles, and/or other types of items that tend to reflect the traits of users. Event data reflective of other types of user actions, such as item-detail-page viewing events, browse node visits, search query submissions, and/or web browsing patterns may additionally or alternatively be considered. By taking such event data into consideration, the matching service reduces the burden on users to explicitly supply personal profile information, and reduces poor results caused by exaggerations and other inaccuracies in such profile information.

主权项

1. A computer-implemented method of matching users to user communities, the method comprising:
for each of a plurality of users of an electronic catalog, storing event data representing user-generated events that reflect user affinities for particular items represented in the electronic catalog; storing information about a plurality of user communities, each of which corresponds to a respective subset of the plurality of users and comprises multiple users; generating a score that reflects a degree to which item preferences of a first user of the plurality of users are similar to item preferences associated with a first user community of said plurality of user communities, wherein generating the score comprises analyzing the event data associated with the first user and event data associated with members of the first user community; and based at least in part on the score, determining whether to suggest the first user community to the first user; wherein the method is performed by one or more computers, wherein generating the score further comprises identifying a first plurality of items that characterize item preferences of the first user community, and calculating a degree to which the first plurality of items are related to a set of items ordered by the first user; and wherein calculating the degree to which the first plurality of items are related to the set of items ordered by the first user comprises accessing item similarity data to determine whether the first plurality of items are similar to the set of items ordered by the first user.