LI Chao1,2, ZHOU Tao1, HUANG Junming3, CHENG Xueqi3, SHEN Huawei3
1. University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China;
2. Beijing Baifendian Information Technology Co., Ltd. Beijing 100080, China;
3. CAS Key Lab of Network Data Science and Technology, Institute of Computing Technology,
Chinese Academy Sciences, Beijing 100190, China)
Abstract:The widely use of personalized recommender systems on online shopping websites results in great profits and enhanced user experiences. However, since a users behaviors usually scatter cross multiple different websites, it becomes difficult to provide accurate recommendations when a recommender system sees a section of his behaviors on a single website. We propose a new recommendation algorithm that transfers behaviors across different websites to calculate similarities between users on different websites. Our algorithm overcomes the sparsity and cold-start problem in recommender systems with a significant accuracy improvment, outperforming traditional algorithms that applied on a single website only.
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