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Information Technology Journal
  Year: 2011 | Volume: 10 | Issue: 8 | Page No.: 1587-1593
DOI: 10.3923/itj.2011.1587.1593
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Clique Discovery Based on User Similarity for Online Shopping Recommendation

Qing Yang, Ping Zhou, Huibing Zhang and Jingwei Zhang

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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  •    Responses of Chinese Consumers to Corporate Advertising Themes: Cue Applicability and Contextual Priming Effects
  •    E-Commerce: A Study on Internet Shopping in Malaysia
  •    SMS Advertisement: Competitve to Gulf Market?
  •    Frequent Browsing Patterns Mining Based on Dependency for Online Shopping
How to cite this article:

Qing Yang, Ping Zhou, Huibing Zhang and Jingwei Zhang, 2011. Clique Discovery Based on User Similarity for Online Shopping Recommendation. Information Technology Journal, 10: 1587-1593.

DOI: 10.3923/itj.2011.1587.1593






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