Trust-enhanced Blog Recommender System: iTrustUn Integrated Approach Based on Multi-faceted Trust and Collaborative Filtering
Date Issued
2008
Date
2008
Author(s)
Peng, Ting-Chun
Abstract
The evolution of the Internet has given people access to information in a way never previously imagined; yet, ironically, it has given rise to the problem of information overload. Fortunately, the advent of recommender systems has relieved people of much of the effort required to find desired information. Blogs represent a new killer application on the Internet that gives users a channel to express themselves and share their knowledge and feelings with other people worldwide. The number of new blogs is growing exponentially. However, due to the diverse subjects covered by bloggers, it is difficult for readers to find blogs containing articles that fit their interests or information needs from the hundreds of thousands, possibly millions, of blogs on the Internet. Currently, most blog recommendation websites only provide search functions based on different types of blogs. In other words, they do not provide any customized or personalized blog article recommendations.iven the need to ease information overload in the blog domain, we have modified some existing approaches, and herein propose a novel trust-enhanced collaborative filtering approach that integrates multi-faceted trust based on article types and user similarity. We also designed an online blog article recommender system, called iTrustU to evaluate whether our proposed approach can improve the accuracy and quality of recommendations. During a 45-day online experiment with 179 participants from the Internet, we found that our system achieved good outcomes in both recommendation accuracy and user satisfaction. In contrast to traditional collaborative filtering approaches, which only consider user similarity or trust information, our integrated approach yields a significantly higher accuracy, especially for cold start users. Through statistical analysis, we prove that in the blogosphere community, trust and similarity among bloggers/readers exhibit a significantly positive correlation. This result is the same as that of past research. Our research results show that, through the exploitation and inference of trust relationships in a trust network, we can provide more effective recommender systems in terms of user satisfaction. The proposed approach not only applies to the blogosphere, but also to any online social community or commercial shopping/auction websites when trust relationships already exist between users on the fly.
Subjects
recommender system
trust
collaborative filtering
blogs
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