A Method to Combine Multiple Recommendation Lists

Abstract

Many studies have reported that combining multiple recommender systems improves their accuracy. In such a combination process, it is important to combine algorithms with different properties properly. However, many of the existing Hybrid Recommender Systems (HRS) can only combine specific algorithms. This study proposed a new HRS, Rescoring Hybrid Recommender System (RHRS), that integrate arbitrary recommendation lists. RHRS can integrate not only collaborative filtering but also popularity rankings and new arrivals lists into one list. It has the following features. (1) Unify the definition of the recommended score of an item in each recommendation list by scoring according to each list’s position. (2) Define the combined weight of the recommendation list as a function of the recommending situation. (3) Optimize the weight of the recommendation list according to the situation. We verified this RHRS with Netflix dataset and confirmed the following results. (1) RHRS has higher recommendation accuracy than existing HRS. (2) RHRS achieves both accuracy of the popularity rankings and diversity of the recommendation list. (3) RHRS recommends items to new users based on popularity ranking and uses collaborative filtering to reflect users’ usage history.