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Modeling Implicit Communities in Recommender Systems

  • Lin XiaoEmail author
  • Gu Zhaoquan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10570)

Abstract

In recommender systems, a group of users may have similar preferences on a set of items. As the groups of users and items are not explicitly given, these similar-preferences groups are called implicit communities (where users inside same communities may not necessarily know each other).

Implicit communities can be detected with users’ rating behaviors. In this paper, we propose a unified model to discover the implicit communities with rating behaviors from recommender systems.

Following the spirit of Latent Factor Model, we design a bayesian probabilistic graphical model which generates the implicit communities, where the latent vectors of users/items inside the same community follow the same distribution. An implicit community model is proposed based on rating behaviors and a Gibbs Sampling based algorithm is proposed for corresponding parameter inferences. To the best of our knowledge, this is the first attempt to integrate the rating information into implicit communities for recommendation.

We provide a linear model (matrix factorization based) and a non-linear model (deep neural network based) for community modeling in recsys.

Extensive experiments on seven real-world datasets have been conducted in comparison with 14 state-of-art recommendation algorithms. Statistically significant improvements verify the effectiveness of the proposed implicit community based models. They also show superior performances in cold-start scenarios, which contributes to the application of real-life recommender systems.

Keywords

Recommender systems Implicit community Gibbs sampling 

Notes

Acknowledgement

This work is supported in part by China Grant U1636215, 61572492, and the Hong Kong Scholars Program.

References

  1. 1.
    Barbieri, N., Manco, G., Ritacco, E.: Probabilistic approaches to recommendations. Synth. Lect. Data Min. Knowl. Discov. 5(2), 1–197 (2014)CrossRefGoogle Scholar
  2. 2.
    Beutel, A., Ahmed, A., Smola, A.J.: ACCAMS: additive co-clustering to approximate matrices succinctly. In: Proceedings of the 24th International Conference on World Wide Web, pp. 119–129. International World Wide Web Conferences Steering Committee (2015)Google Scholar
  3. 3.
    Beutel, A., Murray, K., Faloutsos, C., Smola, A.J.: CoBaFi: Collaborative bayesian filtering. In: Proceedings of the 23rd International Conference on World Wide Web, WWW 2014, pp. 97–108. ACM, New York (2014)Google Scholar
  4. 4.
    Borg, I., Groenen, P.J.F.: Modern multidimensional scaling: theory and applications. J. Educ. Measur. 40(3), 277–280 (2003)CrossRefGoogle Scholar
  5. 5.
    Dhillon, I.S.: Co-clustering documents and words using bipartite spectral graph partitioning. In: KDD 2001, pp. 269–274. ACM, New York (2001)Google Scholar
  6. 6.
    Good, B.H., De Montjoye, Y., Clauset, A.: The performance of modularity maximization in practical contexts. Phys. Rev. E 81(4), 46106 (2009)MathSciNetCrossRefGoogle Scholar
  7. 7.
    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 the Twenty-Ninth AAAI Conference on Artificial Intelligence, 25–30 January 2015, Austin, Texas, USA, pp. 123–129 (2015)Google Scholar
  8. 8.
    Hoyer, P.O.: Non-negative matrix factorization with sparseness constraints. J. Mach. Learn. Res. 5, 1457–1469 (2004)MathSciNetzbMATHGoogle Scholar
  9. 9.
    Jamali, M., Ester, M.: A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of the 2010 ACM Conference on Recommender Systems, RecSys 2010, Barcelona, Spain, 26–30 September 2010, pp. 135–142 (2010)Google Scholar
  10. 10.
    Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 426–434. ACM (2008)Google Scholar
  11. 11.
    Lee, J., Kim, S., Lebanon, G., Singer, Y., Bengio, S.: LLORMA: local low-rank matrix approximation. J. Mach. Learn. Res. 17(15), 1–24 (2016)MathSciNetzbMATHGoogle Scholar
  12. 12.
    Ma, H., Yang, H., Lyu, M.R., King, I.: SoRec: social recommendation using probabilistic matrix factorization. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management, pp. 931–940. ACM (2008)Google Scholar
  13. 13.
    Ma, H., Zhou, D., Liu, C., Lyu, M.R., King, I.: Recommender systems with social regularization. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 287–296. ACM (2011)Google Scholar
  14. 14.
    Rennie, J.D., Srebro, N.: Fast maximum margin matrix factorization for collaborative prediction. In: Proceedings of the 22nd International Conference on Machine Learning, pp. 713–719. ACM (2005)Google Scholar
  15. 15.
    Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: NIPS, vol. 1, pp. 1–2 (2007)Google Scholar
  16. 16.
    Salakhutdinov, R., Mnih, A.: Bayesian probabilistic matrix factorization using markov chain monte carlo. In: Proceedings of the 25th International Conference on Machine Learning, pp. 880–887. ACM (2008)Google Scholar
  17. 17.
    Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, pp. 285–295. ACM (2001)Google Scholar
  18. 18.
    Shan, H., Banerjee, A.: Bayesian co-clustering. In: Eighth IEEE International Conference on Data Mining, ICDM 2008, pp. 530–539. IEEE (2008)Google Scholar
  19. 19.
    Tang, L., Liu, H.: Community detection and mining in social media. Synth. Lect. Data Min. Knowl. Discov. 2(1), 1–137 (2010)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Yang, B., Lei, Y., Liu, D., Liu, J.: Social collaborative filtering by trust. In: IJCAI 2013, Proceedings of the 23rd International Joint Conference on Artificial Intelligence, Beijing, China, 3–9 August 2013 (2013)Google Scholar
  21. 21.
    Zhang, S., Wang, W., Ford, J., Makedon, F.: Learning from incomplete ratings using non-negative matrix factorization. In: SDM, vol. 6, pp. 548–552. SIAM (2006)Google Scholar
  22. 22.
    Zhang, Y., Zhang, M., Liu, Y., Ma, S.: Improve collaborative filtering through bordered block diagonal form matrices. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2013, pp. 313–322. ACM, New York (2013)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institute of Interdisciplinary Information SciencesTsinghua UniversityBeijingChina
  2. 2.Department of Computer ScienceGuangZhou Univeristy and The University of HongKongHongkongChina

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