中圖分類號(hào)： TP399 文獻(xiàn)標(biāo)識(shí)碼： A DOI：10.16157/j.issn.0258-7998.212086 中文引用格式： 麻天,，余本國，張靜,，等. 基于混合聚類與融合用戶興趣的協(xié)同過濾推薦算法[J].電子技術(shù)應(yīng)用，2022,，48(4)：29-33. 英文引用格式： Ma Tian，Yu Benguo,，Zhang Jing,，et al. Collaborative filtering recommendation algorithm based on hybrid clustering and user preferences fusion[J]. Application of Electronic Technique,，2022,，48(4)：29-33.
Collaborative filtering recommendation algorithm based on hybrid clustering and user preferences fusion
Ma Tian1,，2，Yu Benguo3,，Zhang Jing1，2,，Song Wenai1,，2,，Jing Yu1
1.Software School,，North University of China，Taiyuan 030051,，China； 2.Shanxi Military and Civilian Integration Software Engineering Technology Research Center,，Taiyuan 030051，China,； 3.School of Biomedical Information and Engineering,，Hainan Medical University，Haikou 571199,，China
Abstract： Problems such as low recommendation efficiency and recommendation quality to be improved generally exist in the traditional collaborative filtering recommendation algorithm. In order to improve and solve these problems, the collaborative filtering recommendation algorithm integrates mixed clustering with user interests and preferences, and the recommendation quality has been significantly improved after verification. Firstly, a multiple mixed clustering model of Canopy+ Bi-Kmeans was constructed according to the personal information of users. The proposed mixed clustering model was used to divide all users into multiple clusters, and the interest preferences of each user were fused into the generated clusters to form a new similarity calculation model. Secondly, the weight classification method based on TF-IDF algorithm is used to calculate the weight of users on labels, and the exponential decay function incorporating time coefficient is used to capture the change of users′ interest preference with time. Finally, weighted fusion is used to combine user preferences with mixed clustering model to match more similar neighbor users, calculate project scores and make recommendations. The experimental results show that the proposed method can improve the recommendation quality and reliability.
