Abstract
Event-based online social networks (EBSNs) have attracted millions of users to attend events and join event groups. However, the EBSNs are often overwhelmed with too many events and groups, making it is hard for users to attend events and join groups that interest them. Thus, it is natural to design recommender systems to recommend events and groups to users. One key challenge is that, though users have different kinds of behaviors (e.g., user-event behavior, user-word review behavior, and user-group behavior), these data are very sparse for prediction. To that end, in this paper, we propose a content-based co-factorization machines based method for the two recommendation tasks by co-relating users’ different kinds of behaviors. Besides, to alleviate the data sparsity issue, we also model the content information in the co-factorization machines. Finally, experiments on three real-world datasets show the effectiveness of our proposed model on the two prediction tasks.
This work was supported in part by the National Natural Science Foundation of China (Grant No. 61632007), the Open Project Program of the National Laboratory of Pattern Recognition (NLPR Grant No. 201700017).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Hofmann, T.: Probabilistic latent semantic indexing. In: ACM SIGIR Forum, vol. 51, pp. 211–218. ACM (2017)
Hong, L., Doumith, A.S., Davison, B.D.: Co-factorization machines: modeling user interests and predicting individual decisions in Twitter. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, pp. 557–566. ACM (2013)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)
Li, X., Cheng, X., Su, S., Li, S., Yang, J.: A hybrid collaborative filtering model for social influence prediction in event-based social networks. Neurocomputing 230, 197–209 (2017)
Liu, X., He, Q., Tian, Y., Lee, W.C., McPherson, J., Han, J.: Event-based social networks: linking the online and offline social worlds. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1032–1040. ACM (2012)
Macedo, A.Q., Marinho, L.B., Santos, R.L.: Context-aware event recommendation in event-based social networks. In: Proceedings of the 9th ACM Conference on Recommender Systems, pp. 123–130. ACM (2015)
Mnih, A., Salakhutdinov, R.R.: Probabilistic matrix factorization. In: Advances in Neural Information Processing Systems, pp. 1257–1264 (2008)
Pham, T.A.N., Li, X., Cong, G., Zhang, Z.: A general graph-based model for recommendation in event-based social networks. In: 2015 IEEE 31st International Conference on Data Engineering (ICDE), pp. 567–578. IEEE (2015)
Qiao, Z., Zhang, P., Cao, Y., Zhou, C., Guo, L., Fang, B.: Combining heterogenous social and geographical information for event recommendation. In: AAAI, vol. 14, pp. 145–151 (2014)
Rendle, S.: Factorization machines with libFM. ACM Trans. Intell. Syst. Technol. (TIST) 3(3), 57 (2012)
Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 452–461. AUAI Press (2009)
Sun, P., Wu, L., Wang, M.: Attentive recurrent social recommendation. In: The 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 185–194. ACM (2018)
Wang, C., Blei, D.M.: Collaborative topic modeling for recommending scientific articles. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 448–456. ACM (2011)
Wu, L., Chen, E., Liu, Q., Xu, L., Bao, T., Zhang, L.: Leveraging tagging for neighborhood-aware probabilistic matrix factorization. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pp. 1854–1858. ACM (2012)
Wu, L., et al.: Modeling the evolution of users’ preferences and social links in social networking services. IEEE Trans. Knowl. Data Eng. 29(6), 1240–1253 (2017)
Zhang, W., Wang, J., Feng, W.: Combining latent factor model with location features for event-based group recommendation. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 910–918. ACM (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhao, Y., He, Y., Li, H. (2018). Content-Based Co-Factorization Machines: Modeling User Decisions in Event-Based Social Networks. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11165. Springer, Cham. https://doi.org/10.1007/978-3-030-00767-6_72
Download citation
DOI: https://doi.org/10.1007/978-3-030-00767-6_72
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00766-9
Online ISBN: 978-3-030-00767-6
eBook Packages: Computer ScienceComputer Science (R0)