Abstract
Baseline estimate is an important latent factor for recommendations. The current baseline estimate model is widely used by characterizing both items and users. However, it doesn’t consider different users’ rating criterions and results in predictions may be out of recommendation’s rating range. In this paper, we propose a novel baseline estimate model to improve the current performance, named PBEModel (Proportion-based Baseline Estimate Model), which uses rating proportions to compute the rating personalization. The PBEModel is modeled as a piecewise function according to different rating personalization. In order to verify this new baseline estimate, we apply it into SVD++, and propose a novel SVD++ model named PBESVD++. Experiments based on six real datasets show that the proposed PBEModel is rational and more accurate than current baseline estimate model, and the PBESVD++ has relatively higher prediction accuracy than SVD++.
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References
Goldberg, D., Nichols, D., Oki, B.M., et al.: Using collaborative filtering to weave an information tapestry. Commun. ACM 35(12), 61–70 (1992)
Takács, G., Pilászy, I., Németh, B., et al.: Major components of the gravity recommendation system. ACM SIGKDD Explor. Newsl. 9(2), 80–83 (2007)
Sarwar, B.M., Konstan, J.A., Borchers, A., et al.: Applying knowledge from KDD to recommender systems. Univ. Minnesota, Minneapolis 1(612), 625–4002 (1999)
Deerwester, S., Dumais, S.T., Furnas, G.W., et al.: Indexing by latent semantic analysis. J. Am. Soc. Inform. Sci. 41(6), 391 (1990)
Hofmann, T.: Latent semantic models for collaborative filtering. ACM Trans. Inform. Syst. (TOIS) 22(1), 89–115 (2004)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
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, pp. 426–434. ACM (2008)
Koren, Y.: Collaborative filtering with temporal dynamics. Commun. ACM 53(4), 89–97 (2010)
Kumar, R., Verma, B.K., Rastogi, S.S.: Social popularity based SVD++ recommender system. Int. J. Comput. Appl. 87(14) (2014)
Guo, G., Zhang, J., Yorke-Smith, N.: TrustSVD: collaborative filtering with both the explicit and implicit influence of user trust and of item ratings. In: AAAI, pp. 123–129 (2015)
Wang, S., Ma, Y., Cheng, B., Yang, F.M.: Multi-dimensional QoS prediction for service recommendations. IEEE Trans. Serv. Comput., 12 (2016). doi:10.1109/TSC.2016.2584058
Ma, Y., Xin, X., Wang, S., Li, J., Sun, Q., Yang, F.: QoS evaluation for web service recommendation. China Commun. 12(4), 151–160 (2015)
Wang, S., Zheng, Z., Wu, Z., Micheal, R.L., Yang, F.: Reputation measurement and malicious feedback rating prevention in web service recommendation system. IEEE Trans. Serv. Comput. 8(5), 755–767 (2015)
Jannach, D., Zanker, M., Felfernig, A., et al.: Recommender Systems: an Introduction. Cambridge University Press (2010)
Guo, G.: LibRec: A Java Library for Rcommender Systems[EB/OL] (2016). http://www.librec.net
Acknowledgements
This work is supported by the National Natural Science Foundation of China under Grant No. 61402097, No. 61572123 and No. 61502092; the National Science Foundation for Distinguished Young Scholars of China under Grant No. 61225012 and No. 71325002; the Natural Science Foundation of Liaoning Province of China under Grant No. 201602261; the Fundamental Research Funds for the Central Universities under Grant No. N151708005, and No. N151604001.
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© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Tan, Z., He, L., Li, H., Wang, X. (2017). Rating Personalization Improves Accuracy: A Proportion-Based Baseline Estimate Model for Collaborative Recommendation. In: Wang, S., Zhou, A. (eds) Collaborate Computing: Networking, Applications and Worksharing. CollaborateCom 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 201. Springer, Cham. https://doi.org/10.1007/978-3-319-59288-6_10
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DOI: https://doi.org/10.1007/978-3-319-59288-6_10
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