Application of Improved Collaborative Filtering in the Recommendation of E-commerce Commodities

Dan Chang, Hao Yu Gui, Rui Fan, Ze Zhou Fan, Ji Tian


Problems such as low recommendation precision and efficiency often exist in traditional collaborative filtering because of the huge basic data volume. In order to solve these problems, we proposed a new algorithm which combines collaborative filtering and support vector machine (SVM). Different with traditional collaborative filtering, we used SVM to classify commodities into positive and negative feedbacks. Then we selected the commodities that have positive feedback to calculate the comprehensive grades of marks and comments. After that, we build SVM-based collaborative filtering algorithm. Experiments on Taobao data (a Chinese online shopping website owned by Alibaba) showed that the algorithm has good recommendation precision and recommendation efficiency, thus having certain practical value in the E-commerce industry.


recommendation precision, recommendation efficiency, support vector machine (SVM), collaborative filtering

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