Nonparametric Bayesian Probabilistic Latent Factor Model for Group Recommender Systems

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• 关键词：Group recommender systems ; Collaborative filtering ; Bayesian probabilistic matrix factorisation ; Dirichlet prior
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：10041
• 期：1
• 页码：61-76
• 全文大小：1,924 KB
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• 作者单位：Nipa Chowdhury (19)
  Xiongcai Cai (19) (20)
  
  19. School of Computer Science and Engineering, The University of New South Wales, Sydney, New South Wales, 2052, Australia
  20. Techcul Research, Sydney, Australia
  
• 丛书名：Web Information Systems Engineering ¨C WISE 2016
• ISBN：978-3-319-48740-3
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：10041

文摘

The explosion of the online web encourages online users to participate in group activities. Group recommender systems are essential for recommending items to a group of users based on their common preferences. However, existing group recommender systems do not exploit user interaction within a group and merely work on groups with fixed sizes of users and same levels of similarity among group members, which significantly limits its usage in real world scenarios. In this paper, we propose a novel nonparametric Bayesian probabilistic latent factor model to learn the collective users’ tastes and preferences for group recommendation by exploiting user interaction within a group, which is able to well handle a variety of group sizes and similarity levels. We evaluate the developed model on three publicly available benchmark datasets. The experimental results demonstrate that our method outperforms all baseline methods for group recommendation.