[Objective] By improving the traditional collaborative filtering recommendation algorithm to alleviate the existing data sparseness problem, thus enhance the prediction precision. [Methods] This paper proposes a hybrid collaborative filtering recommender framework and KSUBCF algorithm integrated K-means clustering and Slope One algorithm. Firstly, this algorithm uses the Slope One algorithm based on K-means clustering to predict item default rating. And then, to implement recommendation by the collaborative filtering recommendation algorithm based on users. [Results] The experimental results show that with the increase of neighbors numbers, this algorithm is better than the original Slope One algorithm, which MAE value is reduced by 8.8% to 21% and RMSE value is reduced by 17% to 28.1%. [Limitations] This algorithm still relies on user-project score data matrix. [Conclusions] Compared with other traditional collaborative filtering algorithms, the decreases of the MAE value are 10% and 43.8% respectively and the decreases of the RMSE value are 20.1% and 37.4%. The proposed method can improve the prediction precision.
盈艳, 曹妍, 牟向伟. 基于项目评分预测的混合式协同过滤推荐[J]. 现代图书情报技术, 2015, 31(6): 27-32.
Ying Yan, Cao Yan, Mu Xiangwei. A Hybrid Collaborative Filtering Recommender Based on Item Rating Prediction. New Technology of Library and Information Service, DOI：10.11925/infotech.1003-3513.2015.06.05.
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