CSLIM: Contextual SLIM recommendation algorithms

Context-aware recommender systems (CARS) take contextual con-ditions into account when providing item recommendations. In recent years, context-aware matrix factorization (CAMF) has e-merged as an extension of the matrix factorization technique that also incorporates contextual conditions. In this paper, we intro-duce another matrix factorization approach for contextual recom-mendations, the contextual SLIM (CSLIM) recommendation ap-proach. It is derived from the sparse linear method (SLIM) which was designed for Top-N recommendations in traditional recom-mender systems. Based on the experimental evaluations over sev-eral context-aware data sets, we demonstrate that CLSIM can be an effective approach for context-aware recommendations, in many cases outperforming state-of-the-art CARS algorithms in the Top-N recommendation task