GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Alleviating the sparsity problem of collaborative filtering using trust inferences
iTrust'05 Proceedings of the Third international conference on Trust Management
Emergence of global network property based on multi-agent voting model
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
The bandwagon effect of collaborative filtering technology
CHI '08 Extended Abstracts on Human Factors in Computing Systems
Comparing State-of-the-Art Collaborative Filtering Systems
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Evaluating a recommendation application for online video content: an interdisciplinary study
Proceedings of the 8th international interactive conference on Interactive TV&Video
Automatic news recommendations via profiling
Proceedings of the 3rd international workshop on Automated information extraction in media production
Users' (Dis)satisfaction with the personalTV application: Combining objective and subjective data
Computers in Entertainment (CIE) - Theoretical and Practical Computer Applications in Entertainment
Multimedia Tools and Applications
Automatic news recommendations via aggregated profiling
Multimedia Tools and Applications
Efficient community detection with additive constrains on large networks
Knowledge-Based Systems
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There exist a number of similarity-based recommendation communities, within which similar users' opinions are collected by users' agents to make predictions of their opinions on a new item. Similarity-based recommendation communities suffer from some significant limitations, such as scalability and susceptibility to the noise. In this paper, we propose a trust-based community to overcome these limitations. The trust-based recommendation community incorporates trust into the domain of item recommendation. Experimental results based on a real dataset show that trust-based community manages to outperform its similarity-based counterpart in terms of prediction accuracy, coverage, and robustness in the presence of noise.