文章基本信息

标题：Collaborative Personalized Web Recommender System using Entropy based Similarity Measure
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作者：Harita Mehta ; Shveta Kundra Bhatia ; Punam Bedi 等
期刊名称：International Journal of Computer Science Issues
印刷版ISSN：1694-0784
电子版ISSN：1694-0814
出版年度：2011
卷号：8
期号：6
出版社：IJCSI Press
摘要：On the internet, web surfers, in the search of information, always strive for recommendations. The solutions for generating recommendations become more difficult because of exponential increase in information domain day by day. In this paper, we have calculated entropy based similarity between users to achieve solution for scalability problem. Using this concept, we have implemented an online user based collaborative web recommender system. In this model based collaborative system, the user session is divided into two levels. Entropy is calculated at both the levels. It is shown that from the set of valuable recommenders obtained at level I; only those recommenders having lower entropy at level II than entropy at level I, served as trustworthy recommenders. Finally, top N recommendations are generated from such trustworthy recommenders for an online user.
关键词：Collaborative Web Recommender System; Trustworthy users; Entropy based Similarity.