Item recommendation system which considers user ratings of item clusters

Abstract

Various computer-implemented processes are disclosed for using item clustering methods in the process of generating personalized item recommendations for users. One process involves applying a clustering algorithm to a user's collection of items, and using information about the resulting clusters to select items to use as recommendation sources. Personalized recommendations may then be generated based on the selected source items. Another process involves displaying the clusters of items to the user via a collection management interface that enables the user to rate entire clusters of items. The resulting cluster ratings may be used to select recommendation sources, and/or may otherwise be considered in generating recommendations for the user. Cluster-based processes are also disclosed for filtering and organizing the output of a recommendation engine.