Using Dynamically Promoted Experts for Music Recommendation

Recommender systems have become an invaluable asset to online services with the ever-growing number of items and users. Most systems focused on recommendation accuracy, predicting likable items for each user. Such methods tend to generate popular and safe recommendations, but fail to introduce users to potentially risky, yet novel items that could help in increasing the variety of items consumed by the users. This is known as popularity bias, which is predominant in methods that adopt collaborative filtering. Recently, however, recommenders have started to improve their methods to generate lists that encompass diverse items that are both accurate and novel through specific novelty-driven algorithms or hybrid recommender systems. In this paper, we propose a recommender system that uses the concepts of Experts to find both novel and relevant recommendations. By analyzing the ratings of the users, the algorithm promotes special Experts from the user population to create novel recommendations for a target user. Thus, different users are promoted dynamically to Experts depending on who the recommendations are for. The system used data collected from Last.fm and was evaluated with several metrics. Results show that the proposed system outperforms matrix factorization methods in finding novel items and performs on par in finding simultaneously novel and relevant items. This system can also provide a means to popularity bias while preserving the advantages of collaborative filtering.