Exploring social tagging for personalized community recommendations

Original Paper

Abstract

Users of social Web sites actively create and join communities as a way to collectively share their media content and rich experience with diverse groups of people. In this study we focus on the issue of recommending social communities (or groups) to individual users. We address specifically the potential of social tagging for accentuating users’ interests and characterizing communities. We also discuss some unique methods of improving several techniques that have been adapted for use in the context of community recommendations: collaborative filtering, a random walk model, a Katz influence model, a latent semantic model, and a user-centric tag model. We effectively incorporate social tagging information in each algorithm. We present empirical evaluations using real datasets from CiteULike and Last.fm. Our experimental results demonstrate that the different algorithms incorporated with social tagging offer significant advantages in improving both the recommendation quality and coverage, and demonstrate their feasibility for community recommendations in dealing with sparsity-related limitations.

Keywords

Community recommendations Collaborative filtering Graph-based recommendation Latent semantic analysis Recommender systems Social tagging 

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References

  1. Abel, F., Herder, E., Houben, G.-J., Henze, N., Krause, D.: Cross-system user modeling and personalization on the social Web. In: Brusilovski, P., Chin, D. (eds.) Special Issue on Personalization in Social Web Systems, User Modeling and User-Adapted Interaction (2013)Google Scholar
  2. 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
  3. Baatarjav, E.-A., Phithakkitnukoon, S., Dantu, R.: Group recommendation system for Facebook. In: Meersman, R., Tari, Z., Herrero P. (eds.) Proceedings of On the Move to Meaningful Internet Systems: OTM 2008 Workshops, Monterrey, Mexico, pp. 211–219. Springer, Berlin (2008)Google Scholar
  4. Backstrom, L., Huttenlocher, D., Kleinberg, J., Lan, X.: Group formation in large social networks: membership, growth, and evolution. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 44–54. ACM, New Work (2006)Google Scholar
  5. Bianchini M., Gori M., Scarselli F.: Inside PageRank. ACM Trans. Intern. Technol. 5(1), 92–128 (2005)CrossRefGoogle Scholar
  6. Blei D.M., Ng A.Y., Jordan M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)MATHGoogle Scholar
  7. Breese, J.S., Heckerman, D., Kadie C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Cooper, G.F., Moral, S. (eds.) Proceedings of the 14th Annual Conference on Uncertainty in Artificial Intelligence, Madison, Wisconsin, USA, pp. 43–52. Morgan Kaufmann, San Francisco (1998)Google Scholar
  8. Carmagnola, F., Vernero, F., Grillo P.: SoNARS: A social networks-based algorithm for social recommender systems. In: Houben, G.-J., McCalla, G., Pianesi, F., Zancanaro, M (eds.) Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization, pp. 223–234. Springer, Berlin (2009)Google Scholar
  9. Chen, W.-Y., Chu, J.-C., Luan, J., Bai, H., Wang, Y., Chang, E.Y.: Collaborative filtering for Orkut communities: discovery of user latent behavior. In: Proceedings of the 18th International Conference on World Wide Web, Madrid, Spain, pp. 681–690. ACM, New Work (2009)Google Scholar
  10. Chen, W.-Y., Zhang, D., Chang, E.Y.: Combinational collaborative filtering for personalized community recommendation. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas, Nevada, USA, pp. 115–123. ACM, New Work (2008)Google Scholar
  11. Deerwester S., Dumais S.T., Furnas G.W., Landauer T.K., Harshman R.: Indexing by latent semantic analysis. J. Am. Soc. Inf. Sci. 41(6), 391–407 (1990)CrossRefGoogle Scholar
  12. Derenyi G.I., Farkas I., Vicsek T.: Uncovering the overlapping community structure of complex network in nature and society. Nature 435, 814–818 (2005)CrossRefGoogle Scholar
  13. Deshpande M., Karypis G.: Item-based top-n recommendation algorithms. ACM Trans. Inf. Syst. 22(1), 143–177 (2004)CrossRefGoogle Scholar
  14. Golder S.A., Huberman B.A.: Usage patterns of collaborative tagging systems. J. Inf. Sci. 32(2), 198–208 (2006)CrossRefGoogle Scholar
  15. Gupta M., Li R., Yin Z., Han J.: Survey on social tagging techniques. ACM SIGKDD Explor. Newsl. 12, 58–72 (2010)CrossRefGoogle Scholar
  16. Haveliwala T.H.: Topic-sensitive PageRank: a context-sensitive ranking algorithm for Web search. IEEE Trans. Knowl. Data Eng. 15(4), 784–796 (2003)CrossRefGoogle Scholar
  17. Herlocker J.L., Konstan J.A., Terveen L.G., Riedl J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)CrossRefGoogle Scholar
  18. Hofmann, T.: Probabilistic latent semantic indexing. In: Proceedings of the 22nd ACM SIGIR International Conference on Research and Development in Information Retrieval, Berkeley, California, USA, pp. 50–57. ACM, New Work (1999)Google Scholar
  19. Hotho A., Jäschke R., Schmitz C., Stumme G.: Information retrieval in folksonomies: search and ranking. In: Sure, Y., Domingue, J. (eds) Proceedings of the 3rd European Semantic Web Conference, Budva, Montenegro, pp. 411–426. Springer, Berlin (2006)Google Scholar
  20. Jeh, G., Widom, J.: Scaling personalized Web search. In: Proceedings of the 12th International Conference on World Wide Web, Budapest, Hungary, pp. 271–279. ACM, New Work (2003)Google Scholar
  21. Katz L.: A new status index derived from sociometric analysis. Psychometrika 18(1), 39–43 (1953)MATHCrossRefGoogle Scholar
  22. Kim H.-N., Ji A.-T., Ha I., Jo G.-S.: Collaborative filtering based on collaborative tagging for enhancing the quality of recommendation. Electron. Commer. Res. Appl. 9(1), 73–83 (2009)CrossRefGoogle Scholar
  23. Kleanthous, S., Dimitrova, V.: Adaptive notifications to support knowledge sharing in close-knit virtual communities. In: Brusilovski, P., Chin, D. (eds.) Special Issue on Personalization in Social Web Systems, User Modeling and User-Adapted Interaction (2013)Google Scholar
  24. Konstas, I., Stathopoulos, V., Jose, J.M.: On social networks and collaborative recommendation. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Boston, MA, USA, pp 195–202. ACM, New Work (2009)Google Scholar
  25. Langville A.N., Meyer C.D.: Deeper inside PageRank. Intern. Math. 1(3), 335–380 (2004)MathSciNetMATHGoogle Scholar
  26. Lee, D. H., Brusilovsky, P.: Interest similarity of group members: the case study of Citeulike. In: the WebSci10: Extending the Frontiers of Society On-Line, Raleigh, NC, USA (2010)Google Scholar
  27. Levy M., Sandler M.: Learning latent semantic models for music from social tags. J. New Music Res. 37(2), 137–150 (2008)CrossRefGoogle Scholar
  28. Li, X., Guo, L., Zhao, Y.E.: Tag-based social interest discovery. In: Proceedings of the 17th International Conference on World Wide Web, Beijing, China, pp. 675–684. ACM, New Work (2008)Google Scholar
  29. Liben-nowell D., Kleinberg J.: The link-prediction problem for social networks. J. Am. Soc. Inf. Sci. Technol. 58(7), 1019–1031 (2007)CrossRefGoogle Scholar
  30. Manning, C.D., Raghavan, P., Schütze, H. (eds): Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)MATHGoogle Scholar
  31. Mislove, A., Marcon, M., Gummadi, K.P., Druschel, P., Bhattacharjee, B.: Measurement and analysis of online social networks. In: Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement, San Diego, California, USA, pp. 29–42. ACM, New Work (2007)Google Scholar
  32. Negoescu, R.A., Gatica-Perez, D.: Topickr: flickr groups and users reloaded. In: Proceedings of the 16th ACM International Conference on Multimedia, Vancouver, British Columbia, Canada, pp. 857–860. ACM, New Work (2008)Google Scholar
  33. Newman M.E.J., Girvan M.: Finding and evaluating community structure in networks. Phys. Rev. E 69(2), 206113 (2004)CrossRefGoogle Scholar
  34. Pan, J.-Y., Yang, H.-J., Faloutsos, C., Duygulu, P.: Automatic multimedia cross-modal correlation discovery. In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle, WA, USA, pp. 653–658. ACM, New Work (2004)Google Scholar
  35. Pirolli, P., Kairam, S.: A knowledge-tracing model of learning from a social tagging system. In: Brusilovski, P., Chin, D. (eds.) Special Issue on Personalization in Social Web Systems, User Modeling and User-Adapted Interaction (2013)Google Scholar
  36. Sen, S., Vig, J., Riedl, J.: Tagommenders: Connecting users to items through tags. In: Proceedings of the 18th International Conference on World Wide Web, Madrid, Spain, pp. 671–680. ACM, New Work (2009)Google Scholar
  37. Siersdorfer, S., Sizov, S.: Social recommender systems for web 2.0 folksonomies. In: Proceedings of the 20th ACM Conference on Hypertext and Hypermedia, Torino, Italy, pp. 261–270. ACM, New Work (2009)Google Scholar
  38. Spertus, E., Sahami, M., Buyukkokten, O.: Evaluating similarity measures: a large-scale study in the orkut social network. In: Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, Chicago, Illinois, USA, pp. 678–684. ACM, New Work (2005)Google Scholar
  39. Tso-Sutter, K. H. L., Marinho, L. B., Thieme, L. S.: Tag-aware recommender systems by fusion of collaborative filtering algorithms. In: Proceedings of the 23rd ACM Symposium on Applied Computing, Fortaleza, Ceara, Brazil, pp. 1995–1999. ACM, New Work (2008)Google Scholar
  40. Vasuki, V., Natarajan, N., Lu, Z., Dhillon, I.S.: Affiliation recommendation using auxiliary networks. In: Proceedings of the 4th ACM Conference on Recommender Systems, Barcelona, Spain, pp. 103– 110. ACM, New Work (2010)Google Scholar
  41. Wetzker, R., Zimmermann, C., Bauckhage, C., Albayrak, S.: I tag, you tag: translating tags for advanced user models. In: Proceedings of the 3rd ACM International Conference on Web Search and Data Mining, New York, NY, USA, pp. 71–80. ACM, New Work (2010)Google Scholar
  42. Xu, S., Bao, S., Fei, B., Su, Z., Yu, Y.: Exploring folksonomy for personalized search. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Singapore, pp. 155–162. ACM, New Work (2008)Google Scholar
  43. Zanardi, V., Capra, L.: Social ranking: uncovering relevant content using tag-based recommender systems, In: Proceedings of the 2008 ACM conference on Recommender systems, Lausanne, Switzerland, pp. 51–58. ACM, New Work (2008)Google Scholar
  44. Zheleva, E., Sharara, H., Getoor, L.: Co-evolution of social and affiliation networks. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, pp. 1007–1016. ACM, New Work (2009)Google Scholar
  45. Zhen, Y., Li, W. J., Yeung, D. Y.: TagiCoFi: Tag informed collaborative filtering. In: Proceedings of the 3rd ACM Conference on Recommender Systems, New York, NY, USA, pp. 69–76. ACM, New Work (2009)Google Scholar
  46. Zheng N., Li Q., Liao S., Zhang L.: Which photo groups should I choose A comparative study of recommendation algorithms in Flickr. J. Inf. Sci. 36(6), 733–750 (2010)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.School of Electrical Engineering and Computer ScienceUniversity of OttawaOttawaCanada

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