Abstract
Social relations can help to relieve the dilemmas called cold start and data sparsity in traditional recommender systems. Most of existing social recommendation methods are based on matrix factorization, which has been proven effective. In this paper, we introduce a novel social recommender based on the idea that distance reflects likability. It aims to make users in recommender systems be spatially close to their friends and items they like, and be far away from items they dislike by connecting factorization model and distance metric learning. In our method, the positions of users and items are decided by the ratings and social relations jointly, which can help to find appropriate locations for users who have few ratings. Finally, the learnt metric and locations are used to generate understandable and reliable recommendations. The experiments conducted on the real-world dataset have shown that, compared with methods only based on factorization, our method has advantages on both interpretability and accuracy.
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Acknowledgment
This research is supported by the Basic and Advanced Research Projects in Chongqing (cstc2015jcyjA40049), the National Key Basic Research Program of China (973) (2013CB328903), the National Natural Science Foundation of China (61472021), and the Fundamental Research Funds for the Central Universities (106112014 CDJZR 095502)
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Yu, J., Gao, M., Song, Y., Zhao, Z., Rong, W., Xiong, Q. (2017). Connecting Factorization and Distance Metric Learning for Social Recommendations. In: Li, G., Ge, Y., Zhang, Z., Jin, Z., Blumenstein, M. (eds) Knowledge Science, Engineering and Management. KSEM 2017. Lecture Notes in Computer Science(), vol 10412. Springer, Cham. https://doi.org/10.1007/978-3-319-63558-3_33
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DOI: https://doi.org/10.1007/978-3-319-63558-3_33
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