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Information Technology Journal

Year: 2011 | Volume: 10 | Issue: 8 | Page No.: 1587-1593
Research Article

Clique Discovery Based on User Similarity for Online Shopping Recommendation

Qing Yang
Electronic Engineering and Automation Institute, Guilin University of Electronic Technology, Guilin, 541004, China
Ping Zhou
Electronic Engineering and Automation Institute, Guilin University of Electronic Technology, Guilin, 541004, China
Huibing Zhang
Computer and Control Institute, Guilin University of Electronic Technology, Guilin, 541004, China
Jingwei Zhang
Computer and Control Institute, Guilin University of Electronic Technology, Guilin, 541004, China
Identifying cliques with the same interests is valuable for online shopping which can make the recommendation and advertisements to target different users more accurately and maximize the benefits of advertisers, publishers and users. This study, has proposed an effective and efficient method to discover cliques for online shopping which firstly identifies clique leaders and clusters the most similar users, then computes clique cores among existing clique members and finally generates the complete cliques. A marked improvement is that two key factors, users’ behavioral characteristics and regular purchase information, are unified to discover cliques. This method can also remove effectively most of fake purchases through computing the operation similarity among different goods categories.
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