In order to improve collaborative filtering recommender quality,this paper proposes a hybrid collaborative filtering recommender framework integrated k-means clustering and supervised feature selection and KDICF algorithm.The algorithm uses supervised feature selection methods and techniques to select the item sets strongly related to the predicted item.The item sets constitutes a low-dimensional user-item rating datasets.The unpredicted rating of target user is predicted by the nearest neighbors' rating from the low-dimensional dense user-item rating datasets after k-means clustering.The experimental results show that the hybrid KDICF algorithm has excellent performance.%为了提高协同过滤推荐质量,提出了集成k-means聚类和有监督特征选择的混合式协同过滤推荐框架和KDICF算法.利用有监督特征选择的方法和技术,找出与待预测项目强相关的项目集,将高维稀疏的用户-项目评分数据集转为低维用户-项目评分数据集,并运用k-means聚类,在此基础上寻找近邻用户对目标用户未评分项目进行评分预测.实验结果表明,混合式KDICF算法有着优异的性能.
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