中图分类号： TP399
文献标识码： A
DOI：10.16157/j.issn.0258-7998.212086
中文引用格式： 麻天，余本国，张静，等. 基于混合聚类与融合用户兴趣的协同过滤推荐算法[J].电子技术应用，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

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.

Key words : recommendation algorithm；weight label；time attenuation coefficient；exponential decay function；hybrid clustering