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

• Human-centered computing

  • Collaborative and social computing

    • Collaborative and social computing systems and tools

• Information systems

  • Information retrieval

    • Search engine architectures and scalability

      • Peer-to-peer retrieval

  • World Wide Web

    • Web applications

    • Web searching and information discovery

    • Web services

• Social and professional topics

  • Computing / technology policy

    • Government technology policy