Abstract
Recent years have witnessed the fast growing and ubiquity of social media which has significantly changed the social manner and information sharing in our daily life. Given a user, social (i.e. friend) recommendation and behavior (i.e. item) recommendation are two types of popular services in social media applications. Despite the extensive studies, few existing work has addressed both tasks elegantly and effectively. In this paper, we propose an improved unified framework for Social and Behavior Recommendations with Network Embedding (SBRNE for short). With modeling social and behavior information simultaneously, SBRNE integrates social recommendation and behavior recommendation into a unified framework. By employing users’ latent interests as a bridge, social and behavior information is modeled effectively to improve performance of social and behavior recommendations all together. In addition, an efficient network embedding procedure is introduced as a pre-training step for users’ latent representations to improve effectiveness and efficiency of recommendation tasks. Extensive experiments on real-world datasets demonstrate the effectiveness of SBRNE.
Keywords
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We didn’t compare SBRNE with SREPS [13], since we failed to find an implementation of it.
References
Costa, G., Manco, G., Ortale, R.: A generative Bayesian model for item and user recommendation in social rating networks with trust relationships. In: Calders, T., Esposito, F., Hüllermeier, E., Meo, R. (eds.) ECML PKDD 2014, Part I. LNCS (LNAI), vol. 8724, pp. 258–273. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44848-9_17
Cui, P., Wang, X., Pei, J., Zhu, W.: A survey on network embedding. IEEE Trans. Knowl. Data Eng. (2018). https://doi.org/10.1109/TKDE.2018.2849727
Getoor, L., Diehl, C.: Link mining: a survey. ACM SIGKDD Explor. Newsl. 7(2), 3–12 (2005)
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: Proceedings of AAAI, pp. 123–129 (2015)
Hasan, M.A., Zaki, M.J.: A survey of link prediction in social networks. In: Aggarwal, C. (ed.) Social Network Data Analytics, pp. 243–275. Springer, Boston (2011). https://doi.org/10.1007/978-1-4419-8462-3_9
Hu, G., Dai, X., Huang, Y.S.S., Chen, J.: A synthetic approach for recommendation: combining ratings, social relations, and reviews. In: Proceedings of IJCAI, pp. 1756–1762 (2015)
Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: Proceedings of ICDM, pp. 263–272 (2008)
Jamali, M., Ester, M.: A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of RecSys, pp. 135–142 (2010)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)
Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Am. Soc. Inf. Sci. Technol. 58(7), 1019–1031 (2007)
Lichtenwalter, R., Lussier, J., Chawla, N.: New perspectives and methods in link prediction. In: Proceedings of KDD, pp. 243–252 (2010)
Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)
Liu, C., Zhou, C., Wu, J., Hu, Y., Guo, L.: Social recommendation with an essential preference space. In: Proceedings of AAAI, pp. 346–353 (2018)
Liu, Y., Zhao, P., Liu, X., Wu, M., Duan, L., Li, X.: Learning user dependencies for recommendation. In: Proceedings of IJCAI, pp. 2379–2385 (2017)
Liu, Y., Zhao, P., Sun, A., Miao, C.: A boosting algorithm for item recommendation with implicit feedback. In: Proceedings of IJCAI, pp. 1792–1798 (2015)
Ma, H.: An experimental study on implicit social recommendation. In: Proceedings of SIGIR, pp. 73–82 (2013)
Ma, H.: On measuring social friend interest similarities in recommender systems. In: Proceedings of SIGIR, pp. 465–474 (2014)
Ma, H., Yang, H., Lyu, M., King, I.: SoRec: social recommendation using probabilistic matrix factorization. In: Proceedings of CIKM, pp. 931–940 (2008)
Ma, H., Zhou, D., Liu, C., Lyu, M., King, I.: Recommender systems with social regularization. In: Proceedings of WSDM, pp. 287–296 (2011)
Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: Proceedings of ACM SIGKDD, pp. 701–710, August 2014
Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: Proceedings of NIPS, pp. 1257–1264 (2007)
Tang, J., Hu, X., Gao, H., Liu, H.: Exploiting local and global social context for recommendation. In: Proceedings of IJCAI, pp. 2712–2718 (2013)
Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: Line: Large-scale information network embedding. In: Proceedings of WWW, pp. 1067–1077, May 2015
Wang, C., Satuluri, V., Parthasarathy, S.: Local probabilistic models for link prediction. In: Proceedings of ICDM, pp. 322–331 (2007)
Wang, X., Donaldson, R., Nell, C., Gorniak, P., Ester, M., Bu, J.: Recommending groups to users using user-group engagement and time-dependent matrix factorization. In: Proceedings of AAAI, pp. 1331–1337 (2016)
Wang, X., Hoi, S., Ester, M., Bu, J., Chen, C.: Learning personalized preference of strong and weak ties for social recommendation. In: Proceedings of WWW, pp. 1601–1610 (2017)
Xu, X., Yuruk, N., Feng, Z., Schweiger, T.: Scan: a structural clustering algorithm for networks. In: Proceedings of KDD, pp. 824–833 (2007)
Yang, X., Guo, Y., Liu, Y., Steck, H.: A survey of collaborative filtering based social recommender systems. Comput. Commun. 41, 1–10 (2014)
Yu, Z., Wang, C., Bu, J., Wang, X., Wu, Y., Chen, C.: Friend recommendation with content spread enhancement in social networks. Inf. Sci. 309, 102–118 (2015)
Zhao, T., McAuley, J., King, I.: Leveraging social connections to improve personalized ranking for collaborative filtering. In: Proceedings of CIKM, pp. 261–270 (2014)
Acknowledgments
The work is partially supported by the National Natural Science Foundation of China (Nos. 61802404, 61762078, 61702508, 61663004, 61602438), the CCF-Tencent Rhino-Bird Young Faculty Open Research Fund (No. RAGR20180111). Authors are grateful to the anonymous reviewers for helpful comments.
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Zhao, W., Ma, H., Li, Z., Ao, X., Li, N. (2019). SBRNE: An Improved Unified Framework for Social and Behavior Recommendations with Network Embedding. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11447. Springer, Cham. https://doi.org/10.1007/978-3-030-18579-4_33
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