Using inferred tag ratings to improve user-based collaborative filtering
Qi Qi, Zhenyu Chen, Jia Liu, Chengfeng Hui, Qing Qiang Wu · 2012
User-based collaborative filtering is one of the most widely-used recommender methods. It recommends items to a user according to her similar users' opinions. The key point of user-based collaborative filtering is to compute users' similarities. In traditional user-based collaborative filtering, the similarity between two users is determined by their ratings to co-rated items. In some cases, two users rate few common items, such that the similarity between them may be inaccurate and it results in misleading recommendations.