Towards User Profiling for Web Recommendation

  • Guandong Xu
  • Yanchun Zhang
  • Xiaofang Zhou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3809)


Collaborative recommendation is one of widely used recommendation systems, which recommend items to visitor on a basis of referring other’s preference that is similar to current user. User profiling technique upon Web transaction data is able to capture such informative knowledge of user task or interest. With the discovered usage pattern information, it is likely to recommend Web users more preferred content or customize the Web presentation to visitors via collaborative recommendation. In addition, it is helpful to identify the underlying relationships among Web users, items as well as latent tasks during Web mining period. In this paper, we propose a Web recommendation framework based on user profiling technique. In this approach, we employ Probabilistic Latent Semantic Analysis (PLSA) to model the co-occurrence activities and develop a modified k-means clustering algorithm to build user profiles as the representatives of usage patterns. Moreover, the hidden task model is derived by characterizing the meaningful latent factor space. With the discovered user profiles, we then choose the most matched profile, which possesses the closely similar preference to current user and make collaborative recommendation based on the corresponding page weights appeared in the selected user profile. The preliminary experimental results performed on real world data sets show that the proposed approach is capable of making recommendation accurately and efficiently.


User Profile Collaborative Filter User Session Collaborative Recommendation Probabilistic Latent Semantic Analysis Model 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Agarwal, R., Aggarwal, C., Prasad, V.: A Tree Projection Algorithm for Generation of Frequent Itemsets. Journal of Parallel and Distributed Computing 61, 350–371 (1999)CrossRefGoogle Scholar
  2. 2.
    Agrawal, R., Srikant, R.: Mining Sequential Patterns. In: Chen, P.S. (ed.) Proceedings of the International Conference on Data Engineering (ICDE), pp. 3–14. IEEE Computer Society Press, Taipei (1995)Google Scholar
  3. 3.
    Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. Journal Royal Statist. Soc. B 39, 1–38 (1977)zbMATHMathSciNetGoogle Scholar
  4. 4.
    Han, E., Karypis, G., Kumar, V., Mobasher, B.: Hypergraph Based Clustering in High-Dimensional Data Sets: A Summary of Results. IEEE Data Engineering Bulletin 21, 15–22 (1998)Google Scholar
  5. 5.
    Herlocker, J., Konstan, J., Borchers, A., Riedl, J.: An Algorithmic Framework for Performing Collaborative Filtering. In: Proceedings of the 22nd ACM Conference on Researchand Development in Information Retrieval (SIGIR 1999), Berkeley, CA (1999)Google Scholar
  6. 6.
    Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS) 22, 5–53 (2004)CrossRefGoogle Scholar
  7. 7.
    Jin, X., Zhou, Y., Mobasher, B.: A Unified Approach to Personalization Based on Probabilistic Latent Semantic Models of Web Usage and Content. In: Proceedings of the AAAI 2004 Workshop on Semantic Web Personalization (SWP 2004), San Jose (2004)Google Scholar
  8. 8.
    Joachims, T., Freitag, D., Mitchell, T.: Webwatcher: A tour guide for the world wide web. In: The 15th International Joint Conference on Artificial Intelligence (ICJAI 1997), Nagoya, Japan, pp. 770–777 (1997)Google Scholar
  9. 9.
    Konstan, J., Miller, B., Maltz, D., Herlocker, J., Gordon, L., Riedl, J.: Grouplens: Applying Collaborative Filtering to Usenet News. Communications of the ACM 40, 77–87 (1997)CrossRefGoogle Scholar
  10. 10.
    Mobasher, B.: Web Usage Mining and Personalization. In: Singh, M.P. (ed.) Practical Handbook of Internet Computing. CRC Press, Boca Raton (2004)Google Scholar
  11. 11.
    Mobasher, B., Dai, H., Nakagawa, M., Luo, T.: Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization. Data Mining and Knowledge Discovery 6, 61–82 (2002)CrossRefMathSciNetGoogle Scholar
  12. 12.
    Perkowitz, M., Etzioni, O.: Adaptive Web Sites: Automatically Synthesizing Web Pages. In: Proceedings of the 15th National Conference on Artificial Intelligence, pp. 727–732. AAAI, Madison (1998)Google Scholar
  13. 13.
    Xu, G., Zhang, Y., Zhou, X.: A Latent Usage Approach for Clustering Web Transaction and Building User Profile. In: The First International Conference on Advanced Data Mining and Applications (ADMA 2005), pp. 31–42. Springer, Wuhan (2005)Google Scholar
  14. 14.
    Xu, G., Zhang, Y., Zhou, X.: Using Probabilistic Semantic Latent Analysis for Web Page Grouping. In: 15th International Workshop on Research Issues on Data Engineering: Stream Data Mining and Applications (RIDE-SDMA 2005), Tokyo, Japan (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Guandong Xu
    • 1
  • Yanchun Zhang
    • 1
  • Xiaofang Zhou
    • 2
  1. 1.School of Computer Science and MathematicsVictoria UniversityAustralia
  2. 2.School of Information Technology & Electrical EngineeringUniversity of QueenslandBrisbaneAustralia

Personalised recommendations