文章基本信息

标题：A Hybrid Cluster Based Collaborative Filtering with Tensor Factorization Approach for Recommendation System in Big Data
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作者：M. Kavitha ; A.Mohanapriya
期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2015
卷号：3
期号：9
DOI：10.15680/IJIRCCE.2015. 0309098
出版社：S&S Publications
摘要：Nowadays, Collaborative Filtering (CF) is the most accepted recommendation technique, however manyCF systems suffer from issues like data rating availableness and space dimensionality for neighborhood choice.Therefore, using clustering techniques is a way to reduce time needed for processing these correlations. In this work, ahybrid Agglomerative Hierarchical Cluster based CF approach with Tensor factorization (AHC-CF-TF) is projected tosolve these issues, which exploits context variables to factorize users, items and domains into latent feature vectors.This approach hybrids clustering and a new tensor factoring based technique to reinforce the effectiveness of CF.Further, operational on the tensor composed of the overall and aspect ratings and this approach is in a position tocapture the intrinsic relationships between users, items, and aspects, and provide correct predictions on unknownratings. The experimental results on a big dataset show that the proposal improves the prediction accuracy whencompared to baseline strategies
关键词：Collaborative Filtering; Recommendation System; Tensor Factorization; Agglomerative Hierarchical;Clustering; Big data application