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：2
DOI：10.15680/ijircce.2015.0302077
出版社：S&S Publications
摘要：Recommender systems suggest people items or services of their interest and proved to be an importantsolution to information overload problem. The big problem of collaborative filtering is its. In order to solve scalabilityproblem, we can implement the Collaborative Filtering algorithm on the cloud computing platform. Recommendationsystems are very important in the fields of E-commerce and other Web-based services. One of the main difficulties isdynamically providing high-quality recommendation on sparse data. In this paper, a novel dynamic personalizedrecommendation algorithm is proposed, in which information contained in both ratings and profile contents are utilizedby exploring latent relations between ratings, a set of dynamic features are designed to describe user preferences inmultiple phases, and finally a recommendation is made by adaptively weighting the features. Experimental results onpublic datasets show that the proposed algorithm has satisfying performance.
