关键词: 推荐系统, 协同过滤, 项目分类, 用户群体兴趣, 用户加权相似性

Abstract: Similarity calculation method in traditional collaborative filtering is inaccuracy due to the extreme sparsity of user rating data. To address this problem, a collaborative filtering algorithm based on item classification and user group interest is proposed. The algorithm first classifies items by item classification, and then calculates the user similarity matrix and user group interest of each item classification by rating data to construct the user weighted similarity matrix. It obtains the nearest neighbors of target user by user weighted similarity matrix, and generates recommendations. The experimental results show that this algorithm can effectively improve the quality of the recommendation of recommender systems, and get better recommend results.

Key words: recommender systems, collaborative filtering, item classification, user group interest, user weighted similarity