Trust-based agent community for collaborative recommendation

Abstract

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.