Predicting category accesses for a user in a structured information space

Mao Chen, Andrea Suzanne LaPaugh, Jaswinder Pal Singh

Research output: Contribution to journalConference articlepeer-review

27 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)65-72
Number of pages8
JournalSIGIR Forum (ACM Special Interest Group on Information Retrieval)
StatePublished - Dec 1 2002
EventProceedings of the Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval - Tampere, Finland
Duration: Aug 11 2002Aug 15 2002

All Science Journal Classification (ASJC) codes

  • Management Information Systems
  • Hardware and Architecture

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