Similarity of users' (content-based) preference models for Collaborative filtering in few ratings scenario

Abstract

Collaborative filtering is an efficient way to find best objects to recommend. This technique is particularly useful when there is a lot of users that rated a lot of objects.

In this paper, we propose a method that improve the Collaborative filtering in situations, where the number of ratings or users is small. The pro- posed approach is experimentally evaluated on real datasets with very convincing results.

Keywords

• Collaborative filtering
• Preference learning
• Machine learning