User-Weight Model for Item-based Recommendation Systems
Min Gao, Yunqing Fu, Yixiong Chen, Feng Jiang · Journal of Software · 2012
(CF) has been widely used as an effective way to help people cope with information overload. It computes the item-item similarities/differentials and then selects the most similar items for prediction. A weakness of current typical item-based CF approaches is that all users have the same weight in computing the item relationships. In order to improve the recommendation quality, we incorporate users ’ weights based on a relationship model of users into item similarities and differentials computing. In this paper, a model of user relationship, a method for computing users ’ weights, and weight-based item-item similarities/differentials computing approaches are proposed for item-based CF recommendations. Finally, we experimentally evaluate our approach for recommendation and compare it to typical item-based CF approaches based on Adjusted Cosine and Slope One. The experiments show that our approaches can improve the recommendation results of them. Index Terms—personalized recommendation, collaboration filtering, item-based filtering, relationship model I.