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Incorporating Sentiment Analysis for Improved Tag-Based Recommendation

Qingbiao, Zhou and Jie, Fang and Xu, Guandong (2011) Incorporating Sentiment Analysis for Improved Tag-Based Recommendation. In: 2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing (DASC). Springer, Piscataway, N.J., pp. 1222-1227.

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Abstract

Social tagging systems have become as a popular application with the advance of Web 2.0 technologies. By tagging, users annotate and index the resources freely and subjectively, based on their senses of interests, which can improve the performance of the current personalized recommendation systems. In this paper, we propose a sentiment enhanced tag-based recommendation approach by incorporating sentiment analysis of tags that annotated on resources. The presented approach introduces a sentiment enhancement factor to the similarity metric which measures the matching between resources. The evaluation results on a real datasets have demonstrated that our approach can outperform the other compared approaches in terms of recommendation precision.

Item Type: Book Section
ISBN: 9780769546124
Additional Information:

Conference held: Sydney, 12-14 Dec. 2011

Uncontrolled Keywords: ResPubID23725, social annotation systems, tag recommender systems, Internet, social networking, networks, online community, recommender systems, Web 2.0 technologies, improved tag based recommendation, sentiment enhancement factor, social tagging systems
Subjects: Faculty/School/Research Centre/Department > School of Engineering and Science
FOR Classification > 0806 Information Systems
SEO Classification > 8902 Computer Software and Services
Depositing User: VUIR
Date Deposited: 08 Jan 2013 04:53
Last Modified: 08 Jan 2013 04:53
URI: http://vuir.vu.edu.au/id/eprint/9620
DOI: 10.1109/DASC.2011.198
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Citations in Scopus: 0 - View on Scopus

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