Abstract
Recent years have witnessed the rapid growth of event-based social networks (EBSNs) such as Plancast and DoubanEvent. In these EBSNs, followee recommendation which recommends new users to follow can bring great benefits to both users and service providers. In this paper, we focus on the problem of followee recommendation in EBSNs. However, the sparsity and imbalance of the social relations in EBSNs make this problem very challenging. Therefore, by exploiting the heterogeneous nature of EBSNs, we propose a new method called Heterogenous Network based Followee Recommendation (HNFR) for our problem. In the HNFR method, to relieve the problem of data sparsity, we combine the explicit and latent features captured from both the online social network and the offline event participation network of an EBSN. Moreover, to overcome the problem of data imbalance, we propose a Bayesian optimization framework which adopts pairwise user preference on both the social relations and the events, and aims to optimize the area under ROC curve (AUC). The experiments on real-world data demonstrate the effectiveness of our method.
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References
Adamic, L.A., Adar, E.: Friends and neighbors on the web. Soc. Netw. 25(3), 211–230 (2003)
Barbieri, N., Bonchi, F., Manco, G.: Who to follow and why: link prediction with explanations. In: SIGKDD, pp. 1266–1275 (2014)
Cai, Y., Lau, R.Y.K., Liao, S.S.Y., Li, C., Leung, H., Ma, L.C.K.: Object typicality for effective web of things recommendations. Decis. Support Syst. 63, 52–63 (2014)
Cai, Y., Leung, H., Li, Q., Min, H., Tang, J., Li, J.: Typicality-based collaborative filtering recommendation. IEEE Trans. Knowl. Data Eng. 26(3), 766–779 (2014)
Chen, J., Geyer, W., Dugan, C., Muller, M.J., Guy, I.: Make new friends, but keep the old: recommending people on social networking sites. In: CHI, pp. 201–210 (2009)
Guo, G., Zhang, J., Sun, Z., Yorke-Smith, N.: Librec: a java library for recommender systems. In: Posters, Demos, Late-breaking Results and Workshop Proceedings of User Modeling, Adaptation, and Personalization (UMAP 2015) (2015)
Guy, I., Ronen, I., Wilcox, E.: Do you know?: recommending people to invite into your social network. In: IUI, pp. 77–86 (2009)
Hannon, J., Bennett, M., Smyth, B.: Recommending twitter users to follow using content and collaborative filtering approaches. In: RecSys, pp. 199–206 (2010)
Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: ICDM, pp. 263–272 (2008)
Koren, Y., Bell, R.M., Volinsky, C.: Matrix factorization techniques for recommender systems. IEEE Comput. 42(8), 30–37 (2009)
Lee, D.D., Seung, H.S.: Algorithms for non-negative matrix factorization. In: NIPS, pp. 556–562 (2000)
Liben-Nowell, D., Kleinberg, J.M.: The link prediction problem for social networks. In: CIKM, pp. 556–559 (2003)
Liu, X., He, Q., Tian, Y., Lee, W., McPherson, J., Han, J.: Event-based social networks: linking the online and offline socialworlds. In: SIGKDD, pp. 1032–1040 (2012)
Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011, Part II. LNCS, vol. 6912, pp. 437–452. Springer, Heidelberg (2011)
Pan, R., Zhou, Y., Cao, B., Liu, N.N., Lukose, R.M., Scholz, M., Yang, Q.: One-class collaborative filtering. In: ICDM, pp. 502–511 (2008)
Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: bayesian personalized ranking from implicit feedback. In: UAI, pp. 452–461 (2009)
Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: NIPS, pp. 1257–1264 (2007)
Scellato, S., Noulas, A., Mascolo, C.: Exploiting place features in link prediction on location-based social networks. In: SIGKDD, pp. 1046–1054 (2011)
Wan, S., Lan, Y., Guo, J., Fan, C., Cheng, X.: Informational friend recommendation in social media. In: SIGIR, pp. 1045–1048 (2013)
Yuan, G., Murukannaiah, P.K., Zhang, Z., Singh, M.P.: Exploiting sentiment homophily for link prediction. In: RecSys, pp. 17–24 (2014)
Zhao, G., Lee, M., Hsu, W., Chen, W., Hu, H.: Community-based user recommendation in uni-directional social networks. In: CIKM, pp. 189–198 (2013)
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The work was supported by National Natural Science Foundation of China under Grant 61502047.
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Li, S., Cheng, X., Su, S., Jiang, L. (2016). Followee Recommendation in Event-Based Social Networks. In: Gao, H., Kim, J., Sakurai, Y. (eds) Database Systems for Advanced Applications. DASFAA 2016. Lecture Notes in Computer Science(), vol 9645. Springer, Cham. https://doi.org/10.1007/978-3-319-32055-7_3
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DOI: https://doi.org/10.1007/978-3-319-32055-7_3
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