A Collaborative Filtering Algorithm with Phased Forecast
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
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Today collaborative filtering is the most successful recommender system technology. However, in traditional collaborative filtering algorithms, users' interest is considered to be static. That means, in these algorithms, ratings produced at different times are weighted equally, and changes in user purchase interest are not taken into consideration. For this reason, the system may recommend unsatisfactory items when users' interest has changed. To solve this problem, the time factor has been brought into collaborative filtering. In new algorithms, we have divided users' rating history into several equal time stages, and analyzed users' interest distribution in these stages. Experiments have shown that our new algorithm TDCF (Time Distribution Collaborative Filtering Algorithm) substantially improves the precision of item-based collaborative filtering.