ABSTRACT
Context-aware recommender systems (CARS) help improve the effectiveness of recommendations by adapting to users' preferences in different contextual situations. One approach to CARS that has been shown to be particularly effective is Context-Aware Matrix Factorization (CAMF). CAMF incorporates contextual dependencies into the standard matrix factorization (MF) process, where users and items are represented as collections of weights over various latent factors. In this paper, we introduce another CARS approach based on an extension of matrix factorization, namely, the Sparse Linear Method (SLIM). We develop a family of deviation-based contextual SLIM (CSLIM) recommendation algorithms by learning rating deviations in different contextual conditions. Our CSLIM approach is better at explaining the underlying reasons behind contextual recommendations, and our experimental evaluations over five context-aware data sets demonstrate that these CSLIM algorithms outperform the state-of-the-art CARS algorithms in the top-N recommendation task. We also discuss the criteria for selecting the appropriate CSLIM algorithm in advance based on the underlying characteristics of the data.
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Index Terms
- Deviation-Based Contextual SLIM Recommenders
Recommendations
CSLIM: contextual SLIM recommendation algorithms
RecSys '14: Proceedings of the 8th ACM Conference on Recommender systemsContext-aware recommender systems (CARS) take contextual conditions into account when providing item recommendations. In recent years, context-aware matrix factorization (CAMF) has emerged as an extension of the matrix factorization technique that also ...
Deviation-based and similarity-based contextual SLIM recommendation algorithms
RecSys '14: Proceedings of the 8th ACM Conference on Recommender systemsContext-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 ...
Mining contextual movie similarity with matrix factorization for context-aware recommendation
Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in contextContext-aware recommendation seeks to improve recommendation performance by exploiting various information sources in addition to the conventional user-item matrix used by recommender systems. We propose a novel context-aware movie recommendation ...
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