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

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 proposed approach is experimentally evaluated on real datasets with very convincing results.

► We model user preferences using two step model.
► Preference model provides explicit information about user preferences.
► Enhancing Collaborative filtering with user similarity.
► We use real-world datasets for evaluation – Netflix and Sushi.
► The results show clear advantage of StatColl (our proposed method).