The complex dynamics of collaborative tagging
Proceedings of the 16th international conference on World Wide Web
Tag-aware recommender systems by fusion of collaborative filtering algorithms
Proceedings of the 2008 ACM symposium on Applied computing
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Personalized Recommender Systems Integrating Social Tags and Item Taxonomy
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Social tagging in recommender systems: a survey of the state-of-the-art and possible extensions
Artificial Intelligence Review
Developing an ontology-supported information integration and recommendation system for scholars
Expert Systems with Applications: An International Journal
Tags in domain-specific sites: new information?
Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries
A probabilistic definition of item similarity
Proceedings of the fifth ACM conference on Recommender systems
Expert Systems with Applications: An International Journal
Probabilistic approaches to tag recommendation in a social bookmarking network
PRIMA'10 Proceedings of the 13th international conference on Principles and Practice of Multi-Agent Systems
A resource recommendation method based on user taste diffusion model in folksonomies
KSEM'11 Proceedings of the 5th international conference on Knowledge Science, Engineering and Management
Personalised placement in networked video
Proceedings of the 21st international conference companion on World Wide Web
A Resource Recommendation Method Based on User Taste Diffusion Model in Folksonomies
International Journal of Knowledge and Systems Science
Personalized recommender systems integrating tags and item taxonomy
Web Intelligence and Agent Systems
Tag recommendation for social bookmarking: Probabilistic approaches
Multiagent and Grid Systems - Principles and Practice of Multi-Agent Systems
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Recommender Systems is one of the effective tools to deal with information overload issue. Similar with the explicit rating and other implicit rating behaviors such as purchase behavior, click streams, and browsing history etc., the tagging information implies user’s important personal interests and preferences information, which can be used to recommend personalized items to users. This paper is to explore how to utilize tagging information to do personalized recommendations. Based on the distinctive three dimensional relationships among users, tags and items, a new user profiling and similarity measure method is proposed. The experiments suggest that the proposed approach is better than the traditional collaborative filtering recommender systems using only rating data.