利用项目属性和偏好改进协同过滤推荐

协同过滤(CF)推荐系统可以通过了解用户过去的行为向用户推荐项目. 针对现有的CF推荐系统没有利用潜在的项目偏好信息,提出了一种利用项目偏好改进CF的推荐方法. 该方法首先采用K-means算法对用户进行聚类,然后利用用户聚类和效用矩阵构建项目偏好矩阵,最后在基于项目的CF方法中,综合项目评分相似度、项目属性及其偏好特征相似度产生推荐. 实验结果表明,该方法获得了较好的推荐精度,在一定程度上缓解了稀疏问题.

Employing Item Attribute and Preference to Enhance the Collaborative Filtering Recommendation

Recommender systems suggest a few items to the users by understanding their past behaviors. However, the existing collaborative filtering (CF) based recommender systems do not employ the information about latent item preference. In this article, a new CF personalized recommendation approaches was proposed. This approach aims to find user clusters using K-means clustering, and utilizes user clusters and utility matrix to construct item preference matrix,then, combines the item rating similarity, the item attribute and its preference features similarity in the item based CF process to produce recommendations. Experiments show the approach achieves the better result, but also to some extent alleviate the sparsity issue in the recommender systems.