Collaborative Filtering Based on Pairwise User-Item Blocking Structure (PBCF): A General Framework and Its Implementation

Abstract

To our knowledge, all existing collaborative filtering techniques need to find neighbouring relationship between users or items by using some kind of similarity measurement in the feature space. However, a hypothesis hidden behind most existing works is that the similar relationship between users remains static over the whole item sets, which is not true in reality. Users who share similar opinions on some items may have totally different opinions on other items. Users can form many clusters in terms of their opinions on a set of items, However, these clusters may collapse and a new cluster structure will be built in terms their opinions on the new item sets. Analogously, clusters of items formed based on their popularity among a group of users would be disintegrated when encounter a new group of users. In a nutshell, user cluster structure varies across item sets, and vice versa, item cluster structure also varies across user sets.