Employing singular value decomposition and similarity criteria for alleviating cold start and sparse data in context-aware recommender systems

The recommender system’s primary purpose is to estimate the user’s desire and provide a list of items predicted from the appropriate information. Also, context-aware recommendation systems are becoming more and more favorite since they could provide more accurate or personalized recommendation information than traditional recommendation techniques. However, a context-aware recommendation system suffers from two fundamental limitations known as cold start and sparse data. Singular value decomposition has been successfully integrated with some traditional recommendation algorithms. However, the basic singular value decomposition can only extract the feature vectors of users and items, resulting in lower recommendation precision. To improve the recommendation performance and reduce the challenge of cold start and sparse data, we propose a new context-aware recommendation algorithm, named CSSVD. First, in the CSSVD matrix, using the IFPCC and DPCC similarity criteria, the item’s user property attribute matrices are created, respectively, creating the SSVD matrix for the cold start problem. In the second step, through the CWP similarity criterion on the contextual information, the context matrix is created, which according to the SSVD matrix created in the previous step, creates a three-dimensional matrix based on tensor properties, providing the problem of sparse data. We have used the IMDB and STS data collection because of implementing user features, item features, and contextual data for analyzing the recommended method. Experiential results illustrate that the proposed algorithm CSSVD is better than TF, HOSVD, BPR, and CTLSVD in terms of Precision, Recall, F-score, and NDCG measure.Results show the improvement of the recommendations to users through alleviating cold start and sparse data.