Abstract
Recommender systems can help to relieve the dilemma called information overload. Collaborative filtering is a primary approach based on collective historical ratings to recommend items to users. One of the most competitive collaborative filtering algorithm is matrix factorization. In this paper, we proposed an alternative method. It aims to make users be spatially close to items they like and be far away from items they dislike, by connecting matrix factorization and distance metric learning. The metric and latent factors are trained simultaneously and then used to generate reliable recommendations. The experiments conducted on the real-world datasets have shown that, compared with methods only based on factorization, our method has advantage in terms of 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., Rong, W., Song, Y., Fang, Q., Xiong, Q. (2017). Make Users and Preferred Items Closer: Recommendation via Distance Metric Learning. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10638. Springer, Cham. https://doi.org/10.1007/978-3-319-70139-4_30
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DOI: https://doi.org/10.1007/978-3-319-70139-4_30
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