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...

• Type: Journal
• Year: 2002
• Where: SIGIR
• Authors: Mao Chen, Andrea S. LaPaugh, Jaswinder Pal Singh
• Keywords: Access History, Frequency Analysis, Information Technology, Markov Model, SIGIR 2002