Advertisement

Facet-Based User Modeling in Social Media for Personalized Ranking

  • Chen Chen
  • Wu Dongxing
  • Hou Chunyan
  • Yuan Xiaojie
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8416)

Abstract

Micro-blogging service has grown to a popular social media and provides a number of real-time messages for users. Although these messages allow users to access information on-the-fly, users often complain the problems of information overload and information shortage. Thus, a variety of methods of information filtering and recommendation are proposed, which are associated with user modeling. In this study, we propose an effective method of user modeling, facet-based user modeling, to capture user’s interests in social media. We evaluate our models in the context of personalized ranking of microblogs. Experiments on real-world data show that facet-based user modeling can provide significantly better ranking than traditional ranking methods. We also shed some light on how different facets impact user’s interest.

Keywords

Social Medium User Modeling Mean Average Precision Normalize Discount Cumulative Gain Information Shortage 
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.
    Abel, F., Gao, Q., Houben, G.-J., Tao, K.: Analyzing user modeling on twitter for personalized news recommendations. In: Konstan, J.A., Conejo, R., Marzo, J.L., Oliver, N. (eds.) UMAP 2011. LNCS, vol. 6787, pp. 1–12. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  2. 2.
    Chen, J., Nairn, R., Nelson, L., Bernstein, M., Chi, E.: Short and tweet: experiments on recommending content from information streams. In: Proceedings of the 28th International Conference on Human Factors in Computing Systems, pp. 1185–1194 (2010)Google Scholar
  3. 3.
    Chen, K., Chen, T., Zheng, G., Jin, O., Yao, E., Yu, Y.: Collaborative personalized tweet recommendation. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 661–670 (2012)Google Scholar
  4. 4.
    Hong, L., Bekkerman, R., Adler, J., Davison, B.D.: Learning to rank social update streams. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 651–660 (2012)Google Scholar
  5. 5.
    Kapanipathi, P., Orlandi, F., Sheth, A., Passant, A.: Personalized Filtering of the Twitter Stream. In: Proceeding of the 2nd Workshop on Semantic Personalized Information Management: Retrieval and Recommendation, pp. 6–13 (2011)Google Scholar
  6. 6.
    Uysal, I., Croft, W.B.: User oriented tweet ranking: a filtering approach to microblogs. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 2261–2264 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Chen Chen
    • 1
  • Wu Dongxing
    • 1
  • Hou Chunyan
    • 2
  • Yuan Xiaojie
    • 1
  1. 1.Nankai UniversityTianjinChina
  2. 2.Tianjin University of TechnologyTianjinChina

Personalised recommendations