Efficient music recommender system using context graph and particle swarm

Abstract

Music recommender systems is an important field of research because of easy availability and use of online music. The most existing models only focus on explicit data like ratings and other user-item dimensions. A challenging problem in music recommendation is to model a variety of contextual information, such as feedback, time and location. In this article, we proposed a competent hybrid music recommender system (HMRS), which works on context and collaborative approaches. The timestamp is extracted from users listening log to construct a decision context behavior that extracted various temporal features like a week, sessions(as morning, evening or night). We used depth-first-search (DFS) algorithm which traverses the whole graph through the paths in different contexts. Bellman-Ford algorithm provides ranked list of recommended items with multi-layer context graph. We enhanced the process using particle swarm optimization (PSO) which produced highly optimized results. The dataset is used from Last.fm which contains 19,150,868 music listening logs of 992 users (till May, 4th 2009). We extract the properties of music from user’s listening history and evaluate the efficient system to recommend music based on user’s contextual preferences. Our system noticeably delivers the best recommendations regarding recall results when compared to existing methods.