Abstract
Modern social recommendation has been steadily receiving more attention, which utilizes social relations among users to improve the efficiency of recommendation. However, most social recommendation methods only consider simple similarity information of users as social regularization and ignore the improvement of predictors of people’s opinions. Meanwhile, due to the simple characteristics of data in various applications, previous works mostly leverage pointwise methods based on absolute rating assumption to solve the problem. In this paper, we propose a novel Dual Similarity Bayesian Personalized Ranking model to incorporate the similarity information of users and items into our preference predictor function. Having improved the preference predictor, we employ Bayesian Personalized Ranking model as training procedure which is a pairwise method. Empirical results on three public datasets show that our proposed model is an efficient algorithm compared with the state-of-the-art methods.
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Acknowledgements
This work is supported in part by the National Key Basic Research and Department (973) Program of China (No. 2013CB329606), and the National Natural Science Foundation of China (Nos. 71231002, 61375058), and the Co-construction Project of Beijing Municipal Commission of Education.
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Shi, L., Wu, B., Zheng, J., Shi, C., Li, M. (2017). DSBPR: Dual Similarity Bayesian Personalized Ranking. In: Kim, J., Shim, K., Cao, L., Lee, JG., Lin, X., Moon, YS. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2017. Lecture Notes in Computer Science(), vol 10234. Springer, Cham. https://doi.org/10.1007/978-3-319-57454-7_21
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DOI: https://doi.org/10.1007/978-3-319-57454-7_21
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