基于混合聚类与融合用户兴趣的协同过滤推荐算法  

Collaborative filtering recommendation algorithm based on hybrid clustering and user preferences fusion

作　　者：麻天 余本国 张静[1,2] 宋文爱 景昱[1] Ma Tian;Yu Benguo;Zhang Jing;Song Wenai;Jing Yu(Software School,North University of China,Taiyuan 030051,China;Shanxi Military and Civilian Integration Software Engineering Technology Research Center,Taiyuan 030051,China;School of Biomedical Information and Engineering,Hainan Medical University,Haikou 571199,China)

机构地区：[1]中北大学软件学院,山西太原030051 [2]山西省军民融合软件工程技术研究中心,山西太原030051 [3]海南医学院生物医学信息与工程学院,海南海口571199

出　　处：《电子技术应用》2022年第4期29-33,共5页Application of Electronic Technique

基　　金：国家自然科学基金(61602427)。

摘　　要：推荐效率低、推荐质量有待提高等问题普遍存在于传统协同过滤推荐算法中,为了改善并解决这些问题,在协同过滤推荐算法中将混合聚类与用户兴趣偏好融合,经过验证推荐质量有显著提升。首先根据用户的个人相关信息构建Canopy+bi-Kmeans的一种多重混合聚类模型,采用提出的混合聚类模型把所有用户划分成多个聚类簇,将每个用户的兴趣偏好融合到生成的聚类簇中,形成新的相似度计算模型;其次利用基于TF-IDF算法的权重归类方法计算用户对标签的权重,并使融入时间系数的指数衰减函数捕捉用户兴趣偏好随时间的变化;最后使用加权融合将用户偏好和混合聚类模型相结合,匹配到更相似的邻居用户,计算出项目评分并进行推荐。利用公开数据集对比实验证明,提出的方法能够提高推荐质量和推荐可靠性。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.

关 键 词：推荐算法 权重标签 时间衰减系数 指数衰减函数 混合聚类 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]