关键词: 电子商务, 推荐系统, 项目属性, 协同过滤, 推荐精度

Abstract: In order to alleviate the sparsity of the user project rating matrix, the characteristics of the project are analyzed and the project characteristic matrix is introduced based on the traditional scoring matrix of collaborative filtering, then the similarity of the users is calculated based on the similarity of the cosine and the similarity of the user preference to the project attributes, and the importance of both is calculated by a weight. A collaborative filtering algorithm based on item attribute preference is proposed. The results show that when the similarity of the cosine and the similarity of the user preference to the project attributes are equal, the recommendation quality of the recommendation system is the best. When the scoring matrix is ??sparse, the greater the proportion of the similarity of the user preference to the project attributes, the better the quality of recommendation can be achieved. Meanwhile, the collaborative filtering algorithm based on user preferences for project attributes in [MAE] is smaller than the other two traditional collaborative filtering algorithms.

Key words: e-commerce, recommendation system, item attribute, collaborative filtering, recommendation accuracy