基于灰色关联度聚类与标签重叠因子结合的协同过滤推荐方法研究

协同过滤算法是目前被广泛运用在推荐系统领域的最成功技术之一,但是面对用户数量的快速增长及相应的评分数据的缺失,推荐系统中的数据稀疏性问题也越来越明显,严重地影响着推荐的质量和效率。针对传统协同过滤算法中的稀疏性问题,采用了基于灰色关联度的方法对用户评分矩阵进行数据标准化处理,得到用户关联度并形成关联度矩阵;然后对关联矩阵中的用户进行关联度聚类,以减少相似性算法的复杂度;之后利用标签重叠因子对传统计算用户相似性的协同过滤算法进行改进,将重叠因子与用户评分以非线性形式进行组合;最后通过实例改进后的算法在推荐精确度上有着较大的提高。

A collaborative filtering recommendation method based on clustering of gray association degree and factors of tag overlap

Collaborative filtering algorithms are one of the most successful techniques which is widely used in the field of recommendation system. However, with the rapid growth in the number of users and the lack of corresponding rating data, the problem of data sparsity in recommender systems is becoming more and more obvious, which affects recommendation quality and efficiency seriously. To solve the sparse data problem in traditional collaborative filtering algorithms, we propose a method based on gray correlation degree, combination of tag overlap factors and user ratings, which is used for the standardization of the data processing of the user rating matrix, thus obtaining the user correlation degree and the association degree matrix. Clustering based on gray association degree on the correlation matrix of users can reduce computational complexity. Tag overlap factors are also introduced to improve the accuracy of the similarity between users in traditional collaborative filtering algorithms. Experimental results show that the proposed algorithm can greatly improve the prediction accuracy.