An item-based collaborative filtering approach based on balanced rating prediction
Lei Ren, Junzhong Gu, Weiwei Xia · 2011
As a widespread approach in recommender systems, item-based collaborative filtering can predict an active user's interest for a target item based on his interest and the ratings for those similar items to his visited items. As the effect of human's conformity psychology, an individual user's judgment usually tends to follow the general view. The majority of existing item-based collaborative filtering approaches emphasizes the personalized factor of recommendation separately, but ignores the user's general opinions about items. Aiming at this issue of unbalanced recommendation, this paper proposes a refined item-based collaborative filtering approach which employs a balanced rating prediction method incorporating an individual's personalized need with the general opinions. The experimental result shows an improvement in accuracy in contrast to the classic item-based collaborative filtering.