Deviation-Based and Similarity-Based Contextual SLIM Recommendation Algorithms

Context-aware recommender systems (CARS) have been demonstrated to be able to enhance recommendations by adapting users' preferences to different contextual situations. In recent years, several CARS algorithms have been developed to incorporated into the recommender systems. For example, differential context modeling (DCM) was modified based on traditional neighborhood collaborative filtering (NBCF), context-aware matrix factorization (CAMF) coupled contextual dependency with the matrix factorization technique (MF), and tensor factorization directly models contexts as additional dimensions in the multi-dimensional space, etc. CAMF works well but it is difficult to interpret the latent features in the algorithm. DCM is good for explanation but it may only work well on data sets with dense contextual ratings. Recently, we successfully incorporate contexts into Sparse LInear Method (SLIM) and develop contextual SLIM (CSLIM) recommendation algorithms which take advantages of both NBCF and MF. CSLIM are demonstrated as more effective and promising context-aware recommenders. In this work, we provide the introduction on the framework of the CSLIM algorithms, present the current state of the research, and discuss our ongoing future work to develop and improve our CSLIM models for context-aware recommendations.