A novel context-aware recommendation algorithm with two-level SVD in social networks

Abstract With the rapid development of Internet applications and social networks, we have entered an era of big data, and people are hard to effectively find the information they want. Therefore, lots of recommendation algorithms have been proposed to help users select useful and beneficial information, and save their time. Moreover, context-aware recommendation methods are becoming more and more popular since they could provide more accurate or personalized recommendation information, compared with traditional recommendation methods. Singular value decomposition (SVD) has been successfully integrated with some traditional recommendation algorithms. However, the basic SVD can only extract the feature vectors of users and items, which may result in lower recommendation precision. To improve the recommendation performance, we propose a novel context-aware recommendation algorithm with two-level SVD, named CTLSVD. First, CTLSVD applies SVD to divide the rating matrix into the user matrix and item matrix. Second, through extracting more refined factor vectors, CTLSVD further employs SVD to divide the user matrix and item matrix into two matrices, respectively. Finally, CTLSVD utilizes the time as the contextual information to filter the initial unsuitable recommendation results for improving the effectiveness and performance of the final recommendation results. To compare with some well-known recommendation methods, we evaluate CTLSVD on two real datasets, MovieLens and EachMovie. The experimental results demonstrate that our proposed algorithm CTLSVD is better than the traditional recommendation methods in terms of precision, recall and F1-measure.