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 language | English (US) |
|---|---|
| Pages (from-to) | 65-72 |
| Number of pages | 8 |
| Journal | SIGIR Forum (ACM Special Interest Group on Information Retrieval) |
| DOIs | |
| State | Published - 2002 |
| Event | Proceedings of the Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval - Tampere, Finland Duration: Aug 11 2002 → Aug 15 2002 |
All Science Journal Classification (ASJC) codes
- Management Information Systems
- Hardware and Architecture
Keywords
- Access history
- Category access
- Category structure
- Markov model
- Prediction
- Temporal analysis