Abstract
Collaborative filtering (CF) is a highly applicable technology for predicting a user’s rating to a certain item. Recently, some works have gradually switched from modeling users’ rating behaviors alone to modeling both users’ behaviors and preference context beneath rating behaviors such as the set of other items rated by user u. In this paper, we go one step beyond and propose a novel perspective, i.e., k-granularity preference context, which is able to absorb existing preference context as special cases. Based on this new perspective, we further develop a novel and a generic recommendation method called k-CoFi that models k-granularity preference context in collaborative filtering in a principled way. Empirically, we study the effectiveness of factorization with coarse granularity, fine granularity and smooth granularity, and their complementarity, by applying k-CoFi to three real-world datasets. We also obtain some interesting and promising results and useful guidance for practitioners from the experiments.
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References
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.-S.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, WWW 2017, pp. 173–182 (2017)
Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2008, pp. 426–434 (2008)
Pan, W., Ming, Z.: Collaborative recommendation with multiclass preference context. IEEE Intell. Syst. 32(2), 45–51 (2017)
Rendle, S.: Factorization machines with libfm. ACM Trans. Intell. Syst. Technol. 3(3), 57:1–57:22 (2012)
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, CSCW 1994, pp. 175–186 (1994)
Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: Annual Conference on Neural Information Processing Systems, NIPS 2008, pp. 1257–1264 (2008)
Acknowledgements
We thank the support of National Natural Science Foundation of China No. 61502307, No. 61672358 and U1636202, and Natural Science Foundation of Guangdong Province No. 2014A030310268 and No. 2016A030313038.
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Huang, Y., Chen, Z., Li, L., Pan, W., Shan, Z., Ming, Z. (2018). k-CoFi: Modeling k-Granularity Preference Context in Collaborative Filtering. In: Qiu, M. (eds) Smart Computing and Communication. SmartCom 2017. Lecture Notes in Computer Science(), vol 10699. Springer, Cham. https://doi.org/10.1007/978-3-319-73830-7_40
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DOI: https://doi.org/10.1007/978-3-319-73830-7_40
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