一种基于模糊信息熵的协同过滤推荐方法

针对评分数据的稀疏性制约协同过滤推荐性能的情况,提出一种新的相似性度量方法。首先,定义了用户的模糊信息熵以反映用户评分偏好的不确定程度;其次,利用两两用户的模糊互信息衡量用户之间的相似程度;最后,同时考虑用户之间的模糊互信息和用户的模糊信息熵,并设计一种基于模糊信息熵的相似性度量方法以计算用户之间的相似性。在两个公开数据集上的试验结果表明:基于模糊信息熵的相似性度量方法能够降低数据稀疏性的影响,并能显著提高推荐系统的推荐性能。

A method of collaborative filtering recommendation based on fuzzy information entropy

The performance of collaborative filtering was restricted by the sparsity of rating data. To solve this problem, a novel similarity measure based on fuzzy mutual information was proposed. First, the definition of user fuzzy information entropy was given to reflect the uncertainty degree of rating preference. Then, the fuzzy mutual information between users was introduced to measure the similarity degree between users. Finally, the fuzzy information entropy based on similarity measure method was designed to calculate the similarity between users by considering not only the fuzzy mutual information between users but also user fuzzy information entropy. Experimental results on two benchmark data sets showed that the fuzzy information entropy based similarity measure method could reduce the influence of the data sparsity, and the recommendation performance of systems had significant improvements.