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基于联合聚类平滑的协同过滤算法 预览 被引量:7

Collaborative Filtering Algorithm Based on Co-Clustering Smoothing
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摘要 协同过滤是电子商务推荐系统中被广泛采用的技术,但还存在诸如稀疏性、冷启动、可扩展性等制约其进一步发展的瓶颈问题.针对上述问题,提出一种基于联合聚类平滑的协同过滤推荐算法.在该算法中,首先对原始矩阵中的评分模式进行用户和项目2个维度的联合聚类;然后采用联合聚类平滑的方法预测用户对未评分项目的评分值,分别从用户聚类簇、项目聚类簇和联合聚类簇多方面对评分矩阵空缺项进行平滑填充;最后结合基于项目的协同过滤算法查找项目最近邻并进行推荐.实验结果表明,该算法可以有效缓解用户评分数据稀疏带来的不良影响,一定程度上解决冷启动问题,提高预测准确率和推荐质量. Collaborative filtering-based recommender systems have become extremely popular in recent years due to the increase in web-based activities such as e-commerce and online content distribution. However,there exist some bottleneck problems,such as sparsity,cold-start and scalability,which limit the development of collaborative filtering.To address the matter,a novel collaborative filtering algorithm based on co-clustering is proposed.First,co-clustering algorithm is used to simultaneously obtain user and item neighborhoods,and then a smooth filling technique is used on rating matrix based on the average ratings of the co-clusters while taking into account the individual biases of the users and items.Lastly,the similarities between the various items are computed based on the smoothing matrix to identify the set of items to be recommended.The experiment results illustrate that item-based collaborative filtering according to co-clustering smoothing the item correlation matrix will become more accurate,which can effectively relieve the impact of sparse data and improve the quality of recommendation.
作者 韦素云 肖静静 业宁 Wei Suyun;Xiao Jingjing;Ye Ning;College of Information Science and Technology,Nanjing Forestry University;
出处 《计算机研究与发展》 EI CSCD 北大核心 2013年第S2期163-169,共7页 Journal of Computer Research and Development
基金 江苏省“六大人才高峰”基金项目(2011DZXX043) 江苏省自然科学基金项目(BK2012815) 江苏省高等学校大学生实践创新项目(201310298036Z)
关键词 推荐系统 协同过滤 项目相似性 联合聚类 数据平滑 recommendation systems collaborative filtering item similarity co-clustering data smoothing
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参考文献6

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