Recommendation of more interests based on collaborative filtering

Collaborative Filtering is one of the most important techniques in recommender systems. Current researches on Collaborative Filtering focus on how to improve the accuracy. However, it is of the same importance to recommend more potential interests to users because many of them have more expectations for recommendation list besides the accuracy. Current recommender systems did not address this problem. This paper focuses on how to help users find more interests in the recommendation list. We propose an sampling-based algorithm Probabilistic Top-N Selection to recommend potential interests for users, and propose two metrics, average predicted rating and category coverage, to assess the quality of the recommendation list. Then we conduct a series of experiments on Movie Lens dataset, experimental results demonstrate that our algorithm can significantly improve user experience through providing them with more potential interests.