Advertisement

Coupled Linear and Deep Nonlinear Method for Meetup Service Recommendation

  • Shuai Zhang
  • Lina Yao
  • Xiaodong Ning
  • Chaoran Huang
  • Xiwei Xu
  • Shiyan Ou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10966)

Abstract

Meetup brings people with similar interests together to do things that matter to them. For example, it provides a platform for getting people who love hiking, coding, running marathons, learning foreign languages together so that they can help, teach and learn from each other. Thanks to the development of web and mobile technologies, organizing these Meetup groups has become much more easily than before. Meetup has become an ideal tool for enriching one’s social life. In this paper, we proposed a coupled linear and deep nonlinear method for Meetup services recommendation. Our method considers both historical user item interactions and group features by combining linear model with deep neural networks. In addition, we designed a pairwise training algorithm with dynamic negative sampling technique to further enhance the model performance. Experiments on two real-world datasets show that our approach outperforms the compared state-of-the-art methods by a large margin.

Keywords

Recommender systems Deep learning Service recommendation 

References

  1. 1.
    Bengio, Y., Courville, A., Vincent, P.: Representation learning: A review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013)CrossRefGoogle Scholar
  2. 2.
    Deshpande, M., Karypis, G.: Item-based top-n recommendation algorithms. ACM Trans. Inf. Syst. (TOIS) 22(1), 143–177 (2004)CrossRefGoogle Scholar
  3. 3.
    Gantner, Z., Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Mymedialite: a free recommender system library. In: Proceedings of the fifth ACM conference on Recommender systems, pp. 305–308. ACM (2011)Google Scholar
  4. 4.
    Gao, L., Wu, J., Qiao, Z., Zhou, C., Yang, H., Hu, Y.: Collaborative social group influence for event recommendation. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp. 1941–1944. ACM (2016)Google Scholar
  5. 5.
    Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y.: Deep learning, vol. 1. MIT press, Cambridge (2016)Google Scholar
  6. 6.
    He, R., McAuley, J.: VBPR: visual Bayesian personalized ranking from implicit feedback. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, AAAI 2016, pp. 144–150. AAAI Press (2016). http://dl.acm.org/citation.cfm?id=3015812.3015834
  7. 7.
    He, X., Chua, T.S.: Neural factorization machines for sparse predictive analytics. In: Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval, pp. 355–364. ACM (2017)Google Scholar
  8. 8.
    He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, pp. 173–182. International World Wide Web Conferences Steering Committee (2017)Google Scholar
  9. 9.
    Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939 (2015)
  10. 10.
    Hsieh, C.K., Yang, L., Cui, Y., Lin, T.Y., Belongie, S., Estrin, D.: Collaborative metric learning. In: Proceedings of the 26th International Conference on World Wide Web, pp. 193–201. International World Wide Web Conferences Steering Committee (2017)Google Scholar
  11. 11.
    Hsieh, C.K., Yang, L., Wei, H., Naaman, M., Estrin, D.: Immersive recommendation: News and event recommendations using personal digital traces. In: Proceedings of the 25th International Conference on World Wide Web, WWW 2016, pp. 51–62. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland (2016),  https://doi.org/10.1145/2872427.2883006
  12. 12.
    Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: Eighth IEEE International Conference on Data Mining, ICDM 2008, pp. 263–272. IEEE (2008)Google Scholar
  13. 13.
    Kim, D., Park, C., Oh, J., Lee, S., Yu, H.: Convolutional matrix factorization for document context-aware recommendation. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 233–240. ACM (2016)Google Scholar
  14. 14.
    Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
  15. 15.
    Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)CrossRefGoogle Scholar
  16. 16.
    Liu, C.Y., Zhou, C., Wu, J., Xie, H., Hu, Y., Guo, L.: Cpmf: A collective pairwise matrix factorization model for upcoming event recommendation. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 1532–1539. IEEE (2017)Google Scholar
  17. 17.
    Liu, S., Wang, B., Xu, M.: Event recommendation based on graph random walking and history preference reranking. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 861–864. ACM (2017)Google Scholar
  18. 18.
    Liu, S., Wang, B., Xu, M.: Serge: Successive event recommendation based on graph entropy for event-based social networks. IEEE Access (2017)Google Scholar
  19. 19.
    Müngen, A.A., Kaya, M.: A novel method for event recommendation in meetup. In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 959–965. ACM (2017)Google Scholar
  20. 20.
    Ning, X., Karypis, G.: Slim: Sparse linear methods for top-n recommender systems. In: 2011 IEEE 11th International Conference on Data Mining, pp. 497–506, December 2011Google Scholar
  21. 21.
    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, UAI 2009, pp. 452–461. AUAI Press, Arlington, Virginia, United States (2009). http://dl.acm.org/citation.cfm?id=1795114.1795167
  22. 22.
    Shani, G., Gunawardana, A.: Evaluating Recommendation Systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 257–297. Springer, Boston, MA (2011).  https://doi.org/10.1007/978-0-387-85820-3_8CrossRefGoogle Scholar
  23. 23.
    Tay, Y., Anh Tuan, L., Hui, S.C.: Latent relational metric learning via memory-based attention for collaborative ranking. In: In: Proceedings of the 2018 World Wide Web Conference, WWW 2018, pp. 729–739. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland (2018). https://doi.org/10.1145/3178876.3186154
  24. 24.
    Tay, Y., Tuan, L.A., et al.: Multi-pointer co-attention networks for recommendation. CoRR abs/1801.09251 (2018), http://arxiv.org/abs/1801.09251
  25. 25.
    Wang, H., Wang, N., Yeung, D.Y.: Collaborative deep learning for recommender systems. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2015, pp. 1235–1244. ACM, New York (2015). https://doi.org/10.1145/2783258.2783273
  26. 26.
    Wang, Z., Zhang, Y., Li, Y., Wang, Q., Xia, F.: Exploiting social influence for context-aware event recommendation in event-based social networks. In: INFOCOM 2017-IEEE Conference on Computer Communications, pp. 1–9. IEEE (2017)Google Scholar
  27. 27.
    Weston, J., Bengio, S., Usunier, N.: Large scale image annotation: learning to rank with joint word-image embeddings. Mach. Learn. 81(1), 21–35 (2010)MathSciNetCrossRefGoogle Scholar
  28. 28.
    Zhang, S., Yao, L., Sun, A.: Deep learning based recommender system: A survey and new perspectives. arXiv preprint arXiv:1707.07435 (2017)
  29. 29.
    Zhang, S., Yao, L., Xu, X.: Autosvd\(++\): An efficient hybrid collaborative filtering model via contractive auto-encoders. In: PProceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2017, pp. 957–960. ACM, New York (2017). https://doi.org/10.1145/3077136.3080689
  30. 30.
    Zhang, S., Yao, L., Xu, X., Wang, S., Zhu, L.: Hybrid collaborative recommendation via semi-autoencoder. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, E.S.M. (eds.) Neural Information Processing, pp. 185–193. Springer International Publishing, Cham (2017)CrossRefGoogle Scholar
  31. 31.
    Zhang, W., Chen, T., Wang, J., Yu, Y.: Optimizing top-n collaborative filtering via dynamic negative item sampling. In: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval, pp. 785–788. ACM (2013)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Shuai Zhang
    • 1
  • Lina Yao
    • 1
  • Xiaodong Ning
    • 1
  • Chaoran Huang
    • 1
  • Xiwei Xu
    • 2
  • Shiyan Ou
    • 3
  1. 1.School of Computer Science and EngineeringUniversity of New South WalesKensingtonAustralia
  2. 2.Data61, Commonwealth Scientific and Industrial Research OrganisationCanberraAustralia
  3. 3.School of Information ManagementNanjing UniversityNanjingChina

Personalised recommendations