Flexible mixture model for collaborative filtering 论文

摘要

This paper presents a flexible mixture model (FMM) for collaborative filtering. FMM extends existing partitioning/clustering algorithms for collaborative filtering by clustering both users and items together simultaneously without assuming that each user and item should only belong to a single cluster. Furthermore, with the introduction of `preference' nodes, the proposed framework is able to explicitly model how users rate items, which can vary dramatically, even among the users with similar tastes on items.