時間加權之合作過濾推薦系統

Abstract

Nowadays the amount of information in the world is increasing far more quickly than our ability to process them. How people can use their limited time to get interesting information has become an important issue in our daily life. Collabora-tive ‾ltering recommender system is one of the prevailing approaches that can help users to ‾lter unsuitable information. However, traditional collaborative ‾ltering recommender systems do not take the changing behavior of each user's interests into account. This research proposes a new time-weighted collaborative ‾ltering recommender system to capture each user's current interests precisely. The ex-perimental results show that the time-weighted collaborative ‾ltering recommender system outperforms the traditional collaborative ‾ltering recommender system with 11.2% in recommendation accuracy.