Advertisement

TOAST: A Topic-Oriented Tag-Based Recommender System

  • Guandong Xu
  • Yanhui Gu
  • Yanchun Zhang
  • Zhenglu Yang
  • Masaru Kitsuregawa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6997)

Abstract

Social Annotation Systems have emerged as a popular application with the advance of Web 2.0 technologies. Tags generated by users using arbitrary words to express their own opinions and perceptions on various resources provide a new intermediate dimension between users and resources, which deemed to convey the user preference information. Using clustering for topic extraction and incorporating it with the capture of user preference and resource affiliation is becoming an effective practice in tag-based recommender systems. In this paper, we aim to address these challenges via a topic graph approach. We first propose a Topic Oriented Graph (TOG), which models the user preference and resource affiliation on various topics. Based on the graph, we devise a Topic-Oriented Tag-based Recommendation System (TOAST) by using the preference propagation on the graph. We conduct experiments on two real datasets to demonstrate that our approach outperforms other state-of-the-art algorithms.

Keywords

Bipartite Graph Recommender System Preference Propagation Topic Information Personalized Recommendation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Baluja, S., Seth, R., Sivakumar, D., Jing, Y., Yagnik, J., Kumar, S., Ravichandran, D., Aly, M.: Video suggestion and discovery for youtube: taking random walks through the view graph. In: Huai, J., Chen, R., Hon, H.-W., Liu, Y., Ma, W.-Y., Tomkins, A., Zhang, X. (eds.) WWW, pp. 895–904. ACM, New York (2008)CrossRefGoogle Scholar
  2. 2.
    Durao, F., Dolog, P.: Extending a hybrid tag-based recommender system with personalization. In: SAC 2010: Proceedings of the 2010 ACM Symposium on Applied Computing, pp. 1723–1727. ACM, New York (2010)Google Scholar
  3. 3.
    Gemmell, J., Shepitsen, A., Mobasher, M., Burke, R.: Personalization in folksonomies based on tag clustering. In: Proceedings of the 6th Workshop on Intelligent Techniques for Web Personalization and Recommender Systems (July 2008)Google Scholar
  4. 4.
    Guan, Z., Bu, J., Mei, Q., Chen, C., Wang, C.: Personalized tag recommendation using graph-based ranking on multi-type interrelated objects. In: Allan, J., Aslam, J.A., Sanderson, M., Zhai, C., Zobel, J. (eds.) SIGIR, pp. 540–547. ACM, New York (2009)CrossRefGoogle Scholar
  5. 5.
    Guan, Z., Wang, C., Bu, J., Chen, C., Yang, K., Cai, D., He, X.: Document recommendation in social tagging services. In: Rappa, M., Jones, P., Freire, J., Chakrabarti, S. (eds.) WWW, pp. 391–400. ACM, New York (2010)Google Scholar
  6. 6.
    Haveliwala, T.H.: Topic-sensitive pagerank. In: WWW, pp. 517–526 (2002)Google Scholar
  7. 7.
    Hayes, C., Avesani, P.: Using tags and clustering to identify topic-relevant blogs. In: International Conference on Weblogs and Social Media (March 2007)Google Scholar
  8. 8.
    Hotho, A., Jäschke, R., Schmitz, C., Stumme, G.: Folkrank: A ranking algorithm for folksonomies. In: Proc. FGIR 2006 (2006)Google Scholar
  9. 9.
    Lathia, N., Hailes, S., Capra, L.: knn cf: a temporal social network. In: Proceedings of the 2008 ACM Conference on Recommender Systems, RecSys 2008, pp. 227–234. ACM, New York (2008)CrossRefGoogle Scholar
  10. 10.
    Li, L., Yang, Z., Liu, L., Kitsuregawa, M.: Query-url bipartite based approach to personalized query recommendation. In: Fox, D., Gomes, C.P. (eds.) AAAI, pp. 1189–1194. AAAI Press, Menlo Park (2008)Google Scholar
  11. 11.
    Mika, P.: Ontologies are us: A unified model of social networks and semantics. In: Gil, Y., Motta, E., Benjamins, V.R., Musen, M.A. (eds.) ISWC 2005. LNCS, vol. 3729, pp. 522–536. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  12. 12.
    Noll, M.G., Meinel, C.: Web search personalization via social bookmarking and tagging. In: Aberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L.J.B., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) ASWC 2007 and ISWC 2007. LNCS, vol. 4825, pp. 367–380. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  13. 13.
    Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the web. Technical report;, Stanford University (1998)Google Scholar
  14. 14.
    Shepitsen, A., Gemmell, J., Mobasher, B., Burke, R.: Personalized recommendation in social tagging systems using hierarchical clustering. In: RecSys 2008: Proceedings of the 2008 ACM Conference on Recommender Systems, pp. 259–266. ACM, New York (2008)CrossRefGoogle Scholar
  15. 15.
    Sun, J., Qu, H., Chakrabarti, D., Faloutsos, C.: Neighborhood formation and anomaly detection in bipartite graphs. In: ICDM, pp. 418–425 (2005)Google Scholar
  16. 16.
    Xiang, L., Yuan, Q., Zhao, S., Chen, L., Zhang, X., Yang, Q., Sun, J.: Temporal recommendation on graphs via long-and short-term preference fusion. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 723–732. ACM, New York (2010)Google Scholar
  17. 17.
    Zhang, Z., Zhou, T., Zhang, Y.: Personalized recommendation via integrated diffusion on user-item-tag tripartite graphs. Physica A: Statistical Mechanics and its Applications 389(1), 179–186 (2010)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Guandong Xu
    • 1
  • Yanhui Gu
    • 2
  • Yanchun Zhang
    • 1
  • Zhenglu Yang
    • 3
  • Masaru Kitsuregawa
    • 3
  1. 1.School of Engineering and ScienceVictoria UniversityAustralia
  2. 2.Dept. of Information and Communication EngineeringUniversity of TokyoJapan
  3. 3.Institute of Industrial ScienceUniversity of TokyoJapan

Personalised recommendations