Predicting category accesses for a user in a structured information space

ABSTRACT

In a categorized information space, predicting users' information needs at the category level can facilitate personalization, caching and other topic-oriented services. This paper presents a two-phase model to predict the category of a user's next access based on previous accesses. Phase 1 generates a snapshot of a user's preferences among categories based on a temporal and frequency analysis of the user's access history. Phase 2 uses the computed preferences to make predictions at different category granularities. Several alternatives for each phase are evaluated, using the rating behaviors of on-line raters as the form of access considered. The results show that a method based on re-access pattern and frequency analysis of a user's whole history has the best prediction quality, even over a path-based method (Markov model) that uses the combined history of all users.

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.5 On-line Information Services
          Subjects: Data sharing

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.5 On-line Information Services
          Subjects: Web-based services

General Terms:
Algorithms

Keywords:
Markov model, access history, category access, category structure, prediction, temporal analysis