Escaping your comfort zone: A graph-based recommender system for finding novel recommendations among relevant items

• A recommender system based on a positively-related item-graph targeted for novel and relevant recommendations is proposed.
• A live test was performed comparing the proposed system with a state-of-the-art matrix factorization algorithm.
• The proposed system consistently outperforms matrix factorization in finding items that are both novel and relevant.
• By finding novel and relevant items, the system addresses popularity bias commonly found in collaborative filtering-based recommender systems.

Recommender systems have steadily advanced in their ability to filter out unnecessary information and deliver the most relevant data to users. Such recommender systems are being used commercially with popular methods being based on collaborative filtering. While collaborative filtering-based recommenders perform well in terms of accuracy, they lack the ability of finding fresh and novel items, due to the nature of its inner workings. We propose a new graph-based recommender system that uses only positively rated items in users’ profiles to construct a highly-connected, undirected graph, with items as nodes and positive correlations as edges. Using the concept of entropy and the linked items in the graph, the proposed system can find recommendations that are both novel and relevant. We test the system on Last.fm data to recommend music to users and show that the proposed recommender system is indeed able to provide novel recommendations while keeping them relevant to the user profile, consistently outperforming a state-of-the-art matrix factorization-based recommender.