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SIGIR
2002
ACM

Predicting category accesses for a user in a structured information space

8 years 11 months ago
Predicting category accesses for a user in a structured information space
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. Categories and Subject Descriptors H.3.5 [Information Storage and Retrieval]: Online Informat...
Mao Chen, Andrea S. LaPaugh, Jaswinder Pal Singh
Added 23 Dec 2010
Updated 23 Dec 2010
Type Journal
Year 2002
Where SIGIR
Authors Mao Chen, Andrea S. LaPaugh, Jaswinder Pal Singh
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