Skip to main content
View expanded cover

Asia Information Retrieval Symposium

AIRS 2017: Information Retrieval Technology pp 125–137Cite as

A Neural Network Model for Social-Aware Recommendation

Part of the Lecture Notes in Computer Science book series (LNISA,volume 10648)

Abstract

Social-aware recommender systems have been popular with the rapid growth of social media applications. Existing approaches have attempted to accommodate social information into typical Collaborative Filtering methods and achieved significant improvements. Neural networks are gaining increasing interests in information retrieval tasks. However few studies have considered applying neural network in social-aware recommendation tasks. In this paper, we aim to fill this gap and propose a social-aware neural recommender system. Extensive experiments on real-world datasets demonstrate that our model outperforms state-of-art approaches significantly.

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-70145-5_10
  • Chapter length: 13 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   54.99
Price excludes VAT (USA)
  • ISBN: 978-3-319-70145-5
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   69.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1.
Fig. 2.
Fig. 3.

References

  1. Chaney, A.J., Blei, D.M., Eliassi-Rad, T.: A probabilistic model for using social networks in personalized item recommendation. In: Recsys (2015)

    Google Scholar 

  2. Georgiev, K., Nakov, P.: A non-iid framework for collaborative filtering with restricted Boltzmann machines. In: Proceedings of the 30th International Conference on Machine Learning (ICML-2013), pp. 1148–1156 (2013)

    Google Scholar 

  3. 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: AAAI (2015)

    Google Scholar 

  4. Jamali, M., Ester, M.: Trustwalker: a random walk model for combining trust-based and item-based recommendation. In: KDD (2009)

    Google Scholar 

  5. Jamali, M., Ester, M.: Using a trust network to improve top-n recommendation. In: Recsys (2009)

    Google Scholar 

  6. 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, KDD 2008, pp. 426–434. ACM, New York (2008). http://doi.acm.org/10.1145/1401890.1401944

  7. Krohn-Grimberghe, A., Drumond, L., Freudenthaler, C., Schmidt-Thieme, L.: Multi-relational matrix factorization using Bayesian personalized ranking for social network data. In: WSDM (2012)

    Google Scholar 

  8. Larochelle, H., Murray, I.: The neural autoregressive distribution estimator. In: AISTATS, vol. 6, p. 622 (2011)

    Google Scholar 

  9. Levy, O., Goldberg, Y.: Neural word embedding as implicit matrix factorization. In: Advances in Neural Information Processing Systems, pp. 2177–2185 (2014)

    Google Scholar 

  10. Ma, H., Yang, H., Lyu, M.R., King, I.: Sorec: social recommendation using probabilistic matrix factorization. In: CIKM (2008)

    Google Scholar 

  11. Ma, H., Zhou, D., Liu, C., Lyu, M.R., King, I.: Recommender systems with social regularization. In: WSDM (2011)

    Google Scholar 

  12. Rafailidis, D., Crestani, F.: Joint collaborative ranking with social relationships in top-n recommendation. In: CIKM (2016)

    Google Scholar 

  13. 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 

  14. Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: Advances in Neural Information Processing Systems 20, Proceedings of the Twenty-First Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, 3–6 December 2007, pp. 1257–1264 (2007). http://papers.nips.cc/paper/3208-probabilistic-matrix-factorization

  15. Salakhutdinov, R., Mnih, A., Hinton, G.: Restricted Boltzmann machines for collaborative filtering. In: Proceedings of the 24th International Conference on Machine Learning, ICML 2007, pp. 791–798. ACM, New York (2007). http://doi.acm.org/10.1145/1273496.1273596

  16. 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, WWW 2001, pp. 285–295. ACM, New York (2001). http://doi.acm.org/10.1145/371920.372071

  17. Satuluri, V., Parthasarathy, S.: Bayesian locality sensitive hashing for fast similarity search. Proc. VLDB Endowment 5(5), 430–441 (2012)

    CrossRef  Google Scholar 

  18. Sedhain, S., Menon, A.K., Sanner, S., Xie, L.: Autorec: autoencoders meet collaborative filtering. In: Proceedings of the 24th International Conference on World Wide Web, WWW 2015 Companion, pp. 111–112. ACM, New York (2015). http://doi.acm.org/10.1145/2740908.2742726

  19. Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: Line: Large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1067–1077. International World Wide Web Conferences Steering Committee (2015)

    Google Scholar 

  20. Tang, J., Wang, S., Hu, X., Yin, D., Bi, Y., Chang, Y., Liu, H.: Recommendation with social dimensions. In: AAAI (2016)

    Google Scholar 

  21. Wang, X., Lu, W., Ester, M., Wang, C., Chen, C.: Social recommendation with strong and weak ties. In: CIKM (2016)

    Google Scholar 

  22. Weimer, M., Karatzoglou, A., Le, Q.V., Smola, A.J.: COFI RANK - maximum margin matrix factorization for collaborative ranking. In: NIPS (2008)

    Google Scholar 

  23. 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). http://www.aaai.org/ocs/index.php/IJCAI/IJCAI13/paper/view/6750

  24. Yang, B., Lei, Y., Liu, J., Li, W.: Social collaborative filtering by trust. IEEE Trans. Pattern Anal. Mach. Intell. 39(8), 1633–1647 (2016)

    CrossRef  Google Scholar 

  25. Ye, M., Liu, X., Lee, W.C.: Exploring social influence for recommendation: a generative model approach. In: SIGIR (2012)

    Google Scholar 

  26. 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 

  27. Zhao, T., McAuley, J., King, I.: Leveraging social connections to improve personalized ranking for collaborative filtering. In: CIKM (2014)

    Google Scholar 

  28. Zhao, W.X., Huang, J., Wen, J.-R.: Learning distributed representations for recommender systems with a network embedding approach. In: Ma, S., Wen, J.-R., Liu, Y., Dou, Z., Zhang, M., Chang, Y., Zhao, X. (eds.) AIRS 2016. LNCS, vol. 9994, pp. 224–236. Springer, Cham (2016). doi:10.1007/978-3-319-48051-0_17

    CrossRef  Google Scholar 

  29. Zheng, Y., Tang, B., Ding, W., Zhou, H.: A neural autoregressive approach to collaborative filtering. arXiv preprint arXiv:1605.09477 (2016)

Download references

Acknowledgement

This work was supported by Natural Science Foundation (61532011, 61672311) of China and National Key Basic Research Program (2015CB358700).

Author information

Authors and Affiliations

  1. Institute of Interdisciplinary Information Sciences, Tsinghua University, Beijing, China

    Lin Xiao

  2. Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China

    Zhang Min, Liu Yiqun & Ma Shaoping

Authors
  1. Lin Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhang Min
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Liu Yiqun
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ma Shaoping
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lin Xiao .

Editor information

Editors and Affiliations

  1. Korea Institute of Science and Technology Information, Daejeon, Korea (Republic of)

    Won-Kyung Sung

  2. Korea Institute of Science and Technology Information, Daejeon, Korea (Republic of)

    Hanmin Jung

  3. Beijing University of Technology, Beijing, China

    Shuo Xu

  4. Burapha University, Chonburi, Thailand

    Krisana Chinnasarn

  5. Kwansei Gakuin University, Himeji, Hyogo, Japan

    Kazutoshi Sumiya

  6. Korea Institute of Science and Technology Information, Daejeon, Korea (Republic of)

    Jeonghoon Lee

  7. Renmin University of China, Beijing, China

    Zhicheng Dou

  8. Georgetown University, Washington, District of Columbia, USA

    Grace Hui Yang

  9. Konkuk University, Seoul, Korea (Republic of)

    Young-Guk Ha

  10. Korea Institute of Science and Technology Information, Daejeon, Korea (Republic of)

    Seungbock Lee

Rights and permissions

Reprints and Permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Xiao, L., Min, Z., Yiqun, L., Shaoping, M. (2017). A Neural Network Model for Social-Aware Recommendation. In: , et al. Information Retrieval Technology. AIRS 2017. Lecture Notes in Computer Science(), vol 10648. Springer, Cham. https://doi.org/10.1007/978-3-319-70145-5_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-70145-5_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70144-8

  • Online ISBN: 978-3-319-70145-5

  • eBook Packages: Computer ScienceComputer Science (R0)