摘要
针对传统协同过滤算法普遍存在的稀疏性和扩展性问题,提出一种基于模糊聚类的协同过滤算法。利用模糊聚类的方法对项目进行聚类,通过用户-项目评分矩阵计算用户之间的相似度,从中选出与用户最相似的前k个用户,根据这k个用户对当前用户的未评分项目的打分进行预测,选出前n个推荐。实验结果证明,与基于用户的协同过滤算法相比,该算法能提高冷启动问题下的相似度计算精度。
To deal with the sparsity and expansibility of traditional collaborative filtering algorithm,which affects the accuracy of their recommendations,a collaborative filtering algorithm based on fuzzy cluster is proposed in this paper.It applies fuzzy clustering method to cluster the item,and computes the similarity between the users by analyzing the average ratings that the k users rate the items of the clusters.It predicts the ratings of the items that the k users rate based on the ratings of the neighbors that they rate,chooses the first n recommendations.Experimental result demonstrates that the algorithm can improve the accuracy of recommendation under the condition of the extreme sparsity of user rating data.
出处
《计算机工程》
CAS
CSCD
2012年第24期50-52,共3页
Computer Engineering
基金
国家自然科学基金资助项目(61003022
71271126)
上海高校青年教师培养资助计划基金资助项目
关键词
电子商务
推荐系统
模糊聚类
协同过滤
推荐精度
e-commerce
recommendation system
fuzzy clustering
collaborative filtering
recommendation accuracy
