Abstract
Currently, most of the existing recommendation methods treat social network users equally, which assume that the effect of recommendation on a user is decided by the user’s own preferences and social influence. However, a user’s own knowledge in a field has not been considered. In other words, to what extent does a user accept recommendations in social networks need to consider the user’s own knowledge or expertise in the field. In this paper, we propose a novel matrix factorization recommendation algorithm based on integrating social network information such as trust relationships, rating information of users and users’ own knowledge. Specifically, we first use a user’s status (in this paper, status refers to the number of followers and the number of ratings one has done) in a social network to indicate a user’s knowledge in a field since we cannot directly measure a user’s knowledge in the field. Then, we model the final rating of decision-making as a linear combination of the user’s own preferences, social influence and user’s own knowledge. Experimental results on real world data sets show that our proposed approach generally outperforms the state-of-the-art recommendation algorithms that do not consider the knowledge level difference between the users.
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References
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. TKDE 17(6), 734–749 (2005)
Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: UAI (1998)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)
Mnih, A., Salakhutdinov, R.: Probabilistic matrix factorization. In: NIPS (2007)
Ma, H., Yang, H., Lyu, M.R., King, I.: SoRec: social recommendation using probabilistic matrix factorization. In: CIKM (2008)
Ma, H., King, I., Lyu, M.R.: Learning to recommend with social trust ensemble. In: SIGIR, pp. 203–210 (2009)
Jamali, M., Ester, M.: A matrix factorization technique with trust propagation for recommendation in social networks. In: RecSys (2010)
Yang, B., Lei, Y., Liu, D., Liu, J.: Social collaborative filtering by trust. In: IJCAI, pp. 2747–2753 (2013)
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 (2015)
Yang, X., Steck, H., Liu, Y.: Circle-based recommendation in online social networks. In: KDD, pp. 1267–1275 (2012)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: WWW (2001)
Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)
Ungar, L.H., Foster, D.P.: Clustering methods for collaborative filtering. In: AAAI Workshop on Recommendation Systems (1998)
Xue, G.R., Lin, C., Yang, Q., Xi, W., Zeng, H.J., Yu, Y., Chen, Z.: Scalable collaborative filtering using cluster-based smoothing. In: SIGIR (2005)
Yu, Y., Wang, C., Gao, Y., Cao, L., Chen, X.: A coupled clustering approach for items recommendation. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds.) PAKDD 2013. LNCS (LNAI), vol. 7819, pp. 365–376. Springer, Heidelberg (2013). doi:10.1007/978-3-642-37456-2_31
Hofmann, T.: Latent semantic models for collaborative filtering. TOIS 22(1), 89–115 (2004)
Hofmann, T.: Collaborative filtering via Gaussian probabilistic latent semantic analysis. In: SIGIR (2003)
Salakhutdinov, R., Mnih, A., Hinton, G.: Restricted Boltzmann machines for collaborative filtering. In: ICML (2007)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Analysis of recommendation algorithms for e-commerce. In: Proceedings of the 2nd ACM Conference on Electronic Commerce (2000)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Application of dimensionality reduction in recommender system - a case study. In: WebKDD Workshop (2000)
Seung, D., Lee, L.: Algorithms for non-negative matrix factorization. In: NIPS (2001)
Srebro, N., Rennie, J., Jaakkola, T.S.: Maximum-margin matrix factorization. In: NIPS (2004)
Rennie, J.D., Srebro, N.: Fast maximum margin matrix factorization for collaborative prediction. In: ICML (2005)
Golub, G.H., Van Loan, C.F.: Matrix computations, vol. 3. JHU Press (2012)
Nemirovski, A., Juditsky, A., Lan, G., Shapiro, A.: Robust stochastic approximation approach to stochastic programming. SIOPT 19(4), 1574–1609 (2009)
Ma, H., Zhou, D., Liu, C., Lyu, M.R., King, I.: Recommender systems with social regularization. In: WSDM, pp. 287–296 (2011)
Tang, J., Gao, H., Liu, H., Das Sarma, A.: eTrust: understanding trust evolution in an online world. In: KDD, pp. 253–261 (2012)
Acknowledgments
The authors would like to acknowledge the support for this work from the National Natural Science Foundation of China (Grant Nos. 61432008, 61175042, 61403208, 61503178, 61303049) and the Natural Science Foundation of Jiangsu Province of China (BK20150587).
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Yu, Y., Gao, Y., Wang, H., Wang, R. (2016). Joint User Knowledge and Matrix Factorization for Recommender Systems. In: Cellary, W., Mokbel, M., Wang, J., Wang, H., Zhou, R., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2016. WISE 2016. Lecture Notes in Computer Science(), vol 10041. Springer, Cham. https://doi.org/10.1007/978-3-319-48740-3_6
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