Design Considerations for a Social Network-Based Recommendation System (SNRS)

Chapter

Abstract

The effects of homophily among friends have demonstrated their importance to product marketing. However, it has rarely been considered in recommender systems. In this chapter, we propose a new paradigm of recommender systems which can significantly improve performance by utilizing information in social networks including user preference, item likability, and homophily. A probabilistic model, named SNRS, is developed to make personalized recommendations from such information. We extract data from a real online social network, and our analysis of this large dataset reveals that friends have a tendency to select the same items and give similar ratings. Experimental results from this dataset show that SNRS not only improves the prediction accuracy of recommender systems, but also remedies the data sparsity and cold-start issues inherent in collaborative filtering. Furthermore, we propose to improve the performance of SNRS by applying semantic filtering of social networks and validate its improvement via a class project experiment. In this experiment, we demonstrate how relevant friends can be selected for inference based on the semantics of friend relationships and finer-grained user ratings. Such technologies can be deployed by most content providers. Finally, we discuss two trust issues in recommender systems and show how SNRS can be extended to solve these problems.

References

  1. 1.
    Mooney, R.J., Roy, L.: Content-based book recommending using learning for text categorization. In: Proceeding of ACM SIGIR’99 Workshop Recommender Systems: Algorithms and Evaluation (1999)Google Scholar
  2. 2.
    Sarwar, B.M., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of 10th International Conference on World Wide Web (WWW’01) (2001)Google Scholar
  3. 3.
    Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pp. 43–52 (1998)Google Scholar
  4. 4.
    Billsus, D., Pazzani, M.: Learning collaborative information filters. In: Proceedings of International Conference on Machine Learning (1998)Google Scholar
  5. 5.
    Resnick, P., Iakovou, N., Sushak, M., Bergstrom, P., Riedl, J.: GroupLens: An open architecture for collaborative filtering of Netnews. In: Proceedings of Computer Supported Cooperative Work Conference (1994)Google Scholar
  6. 6.
    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)CrossRefGoogle Scholar
  7. 7.
    Melville, P., Mooney, R.J., Nagarajan R.: Content-boosted collaborative filtering for improved recommendations. In: Proceedings of the 18th National Conference on Artificial Intelligence (AAAI-2002), pp. 187–192. Edmonton, Canada, July 2002Google Scholar
  8. 8.
    Sarwar, B.M., Karypis, G., Konstain, J., Riedl, J.: Application of dimensionality reduction in recommender systems – a case study. In: Proceedings of ACM WebKDD Workshop (2000)Google Scholar
  9. 9.
    O’Connor, M., Herlocker, J.: Clustering items for collaborative filtering. In: Proceedings of the ACM SIGIR Workshop on Recommender Systems, Berkeley, CA (1999)Google Scholar
  10. 10.
    Morita, M., Shinoda, Y.: Information filtering based on user behavior analysis and best match text retrieval. In: Proceedings of the 7th Annual Information ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 272–281 (1994)Google Scholar
  11. 11.
    Huang, Z., Chen, H., Zeng, D.: Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Trans. Inf. Syst. 22(1), 116–142 (2004)CrossRefGoogle Scholar
  12. 12.
    He, J., Chu, W.W.: A social network-based recommender system (SNRS). Ann. Inf. Syst. 12 (2010). Special Issue on Data Mining for Social Network Data (AIS-DMSND), pp. 47–74Google Scholar
  13. 13.
    McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: Homophily in social networks. Annu. Rev. Sociol. 27, 415–44 (2001)CrossRefGoogle Scholar
  14. 14.
    Subramani, M.R., Rajagopalan, B.: Knowledge-sharing and influence in online social networks via viral marketing. Commun. ACM 46(12), 300–307 (2003)CrossRefGoogle Scholar
  15. 15.
    Yang, S., Allenby, G.M.: Modeling interdependent consumer preferences. J. Mark. Res. 40, 282–294 (2003)CrossRefGoogle Scholar
  16. 16.
    Jurvetson, S.: What exactly is viral marketing? Red Herring 78, 110–112 (2000)Google Scholar
  17. 17.
    Basu, C., Hirsh, H., Cohen, W.: Recommendation as classification: Using social and content-based information in recommendation. In: Recommender System Workshop’98, pp. 11–15 (1998)Google Scholar
  18. 18.
    Pazzani, M.: A framework for collaborative, content-based, and demographic filtering. Artif. Intell. Rev. 13, 393–408 (1999)CrossRefGoogle Scholar
  19. 19.
    Wang, J., Vires, A.P., Reinders, M.J.T.: Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development on Information Retrieval (SIGIR’06), 6–11 Aug 2006Google Scholar
  20. 20.
    Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: Proceedings of ACM SIGIR, pp. 230–237 (1999)Google Scholar
  21. 21.
    Chandra, B., Gupta, M., Gupta, M.P.: Robust approach for estimating probabilities in Naïve-Bayes classifier. Pattern Recognit. Mach. Intell. 4815, 11–16 (2007)CrossRefGoogle Scholar
  22. 22.
    Lu. Q., Getoor, L.: Link-based classification. In: Proceedings of the 20th International Conference on Machine Learning (ICML), pp. 496–503 (2003)Google Scholar
  23. 23.
    Neville, J., Jensen, D.: Iterative classification in relational data. In: Proceedings of the Workshop on Learning Statistical Models from Relational Data at the 17th National Conference on Artificial Intelligence (AAAI), pp. 13–20 (2000)Google Scholar
  24. 24.
    Sen, P., Getoor, L.: Empirical comparison of approximate inference algorithms for networked data. ICML Workshop on Open Problems in Statistical Relational Learning, Pittsburgh, PA (2006)Google Scholar
  25. 25.
    Carvalho, V., Cohen, W. W.: On the collective classification of email speech acts. Special Interest Group on Information Retrieval (2005)Google Scholar
  26. 26.
    O’Donovan, J., Smyth, B.: Trust in recommender systems. In Proceedings of IUI’05, 9–12 Jan (2005)Google Scholar
  27. 27.
    Massa, P., Avesani, P.: Trust-aware collaborative filtering for recommender systems. In: Proceedings of Federated International Conference on the Move to Meaningful Internet: CoopIS, DOA, ODBASE, pp. 492–508 (2004)Google Scholar
  28. 28.
    Chu, W.W., Chiang, K., Hsu, C.C., Yau, H.: An error-based conceptual clustering method for providing approximate query answers. Commun. ACM 39(13) (1996)Google Scholar
  29. 29.
    Mao, W., Chu, W.W.: Free-text medical document retrieval via phase-based vector space model. In: Proceedings of American Medical Informatics Association (AMIA), Annual Symposium (2002)Google Scholar
  30. 30.
    Zheng, R., Provost, F., Ghose, A.: Social network collaborative filtering: preliminary results. In: Proceedings of the 6th Workshop on eBusiness (WEB2007), Dec 2007Google Scholar
  31. 31.
    Lathia, N., Hailes, S., Capra, L.: The effect of correlation coefficients on communities of recommenders. In: Proceedings of SAC\prime 08, 16–20 March 2008Google Scholar
  32. 32.
    Pazzani, M., Billsus, D.: Learning and revising user profiles: The identification of interesting web sites. Mach Learn 27, 313–331 (1997)CrossRefGoogle Scholar
  33. 33.
    Sinha, R., Swearingen, K.: Comparing recommendations made by online systems and friends. In: Proceedings of the DELOS-NSF Workshop on Personalization and Recommender Systems in Digital Libraries (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Computer Science DepartmentUniversity of CaliforniaLos AngelesUSA

Personalised recommendations