Improved collaborative filtering with intensity-based contraction

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• 作者：Baojiang Cui ; Haifeng Jin ; Zheli Liu…
• 关键词：Recommendation system ; Collaborative filtering ; Electronic commerce ; Intensity ; based contraction ; Scalability
• 刊名：Journal of Ambient Intelligence and Humanized Computing
• 出版年：2015
• 出版时间：October 2015
• 年：2015
• 卷：6
• 期：5
• 页码：661-674
• 全文大小：1,682 KB
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• 作者单位：Baojiang Cui (1) (2)
  Haifeng Jin (1) (2)
  Zheli Liu (3) (4)
  Jiangdong Deng (1) (2)
  
  1. School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China
  2. National Engineering Laboratory for Mobile Network Security, Beijing, China
  3. Department of Computer and Information Security, College of Information Technical Science, Nankai University, Tianjin, China
  4. Key Laboratory of Network Security and Cryptology, Fujian Normal University, Fuzhou, China
  
• 刊物类别：Engineering
• 刊物主题：Computational Intelligence
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1868-5145

文摘

Recommendation systems are essential tools for piquing consumers-interests and stimulating consumption in today’s electronic commerce, and the quality of these systems depends on the employed filtering algorithms. Therefore, improving the performance of these algorithms is an important issue. In this paper, we design an intensity-based contraction (IC) algorithm that works in combination with other machine-learning algorithms in model-based collaborative filtering, which is currently the most popular filtering algorithm. The main challenges for this algorithm are sparseness of the database and lack of scalability. To demonstrate how IC is used, we implemented IC clustering as an example, which can effectively reduce the sparseness of the database and improve the efficiency. Moreover, we created a scalable IC on a MapReduce model, the scalability of which is demonstrated with actual experiments. Keywords Recommendation system Collaborative filtering Electronic commerce Intensity-based contraction Scalability