Recommending People to Follow Using Asymmetric Factor Models with Social Graphs

  • Tianle Ma
  • Yujiu Yang
  • Liangwei Wang
  • Bo Yuan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 223)


Traditional recommendation techniques often rely on the user-item rating matrix, which explicitly represents a user’s preference among items. Recent studies on recommendations in the scenario of social networks still largely follow this principle. However, the challenge of recommending people to follow in social networks has yet to be studied thoroughly. In this paper, by using the utility instead of ratings and randomly sampling the negative cases in the recommendation log to create a balanced training dataset, we apply the popular matrix factorization techniques to predict whether a user will follow the person recommended or not. The asymmetric factor models are built with an extended item set incorporating the social graph information, which greatly improves the prediction accuracy. Other factors such as sequential patterns, CTR bias, and temporal dynamics are also exploited, which produce promising results on Task 1 of KDD Cup 2012.


Social Recommender Systems Matrix Factorization People Recommendation Social Graph Asymmetric Factor Model 



This work was supported by the National Natural Science Foundation of China (No. 60905030) and Upgrading Plan Project of Shenzhen Key Laboratory (No. CXB201005250038A). The authors are also grateful to several colleagues who have provided constructive feedbacks to our work. Besides, we would like to gratefully acknowledge the organizers of KDD Cup 2012 as well as Tencent Inc. for making the datasets available.


  1. 1.
    Niu, Y., Wang, Y., Sun, G., Yue, A., Dalessandro, B., Perlich, C., Hamner, B.: The Tencent Dataset and KDD-Cup’12. In KDD-Cup, Workshop (2012)Google Scholar
  2. 2.
    Jannach, D., Zanker, M., Felfernig, A., Friedrich, G.: Recommender Systems: An Introduction. Cambridge University Press, Cambridge (2010)Google Scholar
  3. 3.
    Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)Google Scholar
  4. 4.
    Yang, X., Steck, H., Guo, Y., Liu, Y.: On Top-k Recommendation using Social Networks. In: Proceedings of 6th ACM Conference on Recommender Systems. ACM, Dublin (2012)Google Scholar
  5. 5.
    Paterek, A.: Improving regularized singular value decomposition for collaborative filtering. In: Proceedings of KDD Cup and Workshop 2007, pp. 5–8. ACM, San Jose (2007)Google Scholar
  6. 6.
    Keshavan, R.H., Montanari, A., Oh, S.: Matrix completion from noisy entries. J. Mach. Learn. Res. 11, 2057–2078 (2010)MathSciNetzbMATHGoogle Scholar
  7. 7.
    Pan, R., Zhou, Y., Cao, B., Liu, N.N., Lukose, R., Scholz, M., Yang, Q.: One-class collaborative filtering. In: Proceedings of the 8th IEEE International Conference on Data Mining, pp. 502–511. IEEE, Pisa (2008)Google Scholar
  8. 8.
    Srebro, N., Jaakkola, T.: Weighted low-rank approximations. In: Proceedings of the 20th International Conference on Machine Learning, pp. 720–727. AAAI Press, Washington, DC (2003)Google Scholar
  9. 9.
    Jahrer, M., Töscher, A., Legenstein, R.: Combining predictions for accurate recommender systems. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 693–702. ACM, Washington, DC (2010)Google Scholar
  10. 10.
    Su, X., Khoshgoftaar T.M.: A Survey of Collaborative Filtering Techniques. Adv. Artif. Intell. (2009)Google Scholar
  11. 11.
    Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to weave an information tapestry. Commun. ACM 35(12), 61–70 (1992)CrossRefGoogle Scholar
  12. 12.
    Melville, P., Mooney, R.J., Nagarajan, R.: Content-boosted collaborative filtering for improved recommendations. In: Proceedings of the 18th National Conference on Artificial Intelligence and 14th Conference on Innovative Application of Artificial Intelligence, pp. 187–192. AAAI Press; MIT Press; Edmonton (2002)Google Scholar
  13. 13.
    Koren, Y.: Collaborative filtering with temporal dynamics. Commun. ACM 53(4), 89–97 (2010)CrossRefGoogle Scholar
  14. 14.
    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, Las Vegas, Nevada (2008)Google Scholar
  15. 15.
    Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. IEEE Comput. 42(8), 30–37 (2009)CrossRefGoogle Scholar
  16. 16.
    Singla, P., Richardson, M.: Yes, there is a correlation: from social networks to personal behavior on the web. In: Proceedings of the 17th International Conference on World Wide Web, pp. 655–664. ACM, Beijing (2008)Google Scholar
  17. 17.
    Massa, P., Avesani, P.: Trust-aware recommender systems. In: Proceedings of the 2007 ACM Conference on Recommender Systems, pp. 17–24. ACM, Minneapolis (2007)Google Scholar
  18. 18.
    Jamali, M., Ester, M.: TrustWalker: a random walk model for combining trust-based and item-based recommendation. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 397–406. ACM, Paris (2009)Google Scholar
  19. 19.
    Ma, H., King, I., Lyu, M.R.: Learning to recommend with social trust ensemble. In: Proceedings of the 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 203–210. ACM, Boston (2009)Google Scholar
  20. 20.
    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, pp. 135–142. ACM, Barcelona (2010)Google Scholar
  21. 21.
    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, Napa Valley (2008)Google Scholar
  22. 22.
    Golbeck, J.A.: Computing and Applying Trust in Web-based Social Networks. University of Maryland at College Park, College Park (2005)Google Scholar
  23. 23.
    Jamali, M., Ester, M.: Using a trust network to improve Top-N recommendation. In: Proceedings of the 2009 ACM Conference on Recommender Systems, pp. 181–188. ACM, New York (2009)Google Scholar
  24. 24.
    Koren, Y.: Factor in the neighbors: scalable and accurate collaborative filtering. ACM Trans. Knowl. Discov. Data 4(1), 1–24 (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Intelligent Computing Lab, Graduate School at ShenzhenTsinghua UniversityShenzhenP. R. China
  2. 2.Huawei Noah’s Ark LabHuawei Technologies Co., Ltd.ShenzhenP. R. China

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