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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)

Abstract

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.

Keywords

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.

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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

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