An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Web Page Recommender System based on Folksonomy Mining for ITNG '06 Submissions
ITNG '06 Proceedings of the Third International Conference on Information Technology: New Generations
Personalized recommendation driven by information flow
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
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
Tag Recommendations in Folksonomies
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Collaborative Filtering Recommender Systems Using Tag Information
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Collaborative filtering in social tagging systems based on joint item-tag recommendations
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
An adaptive spreading activation scheme for performing more effective collaborative recommendation
DEXA'05 Proceedings of the 16th international conference on Database and Expert Systems Applications
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Collaborative tagging has been very popular with the development of the Web 2.0, which helps users manage, share and utilize resources effectively. For various kinds of resources, the way to recommend appropriate resources to right users is the key problem in tagging system. This paper proposes a user taste diffusion model based on the tripartite hypergraph to deal with the tri-relation of user-resource-tag in folksonomies and the data sparsity problem in personalized recommendation. Through the defined tri-relation model and diffusion probability matrix, the user's taste is diffused from itself to other users, resources and tags. When diffusion stops, the candidate resources can be identified then be ranked according to the taste values. As a result the top resources that have not been collected by the given user are selected as the final recommendations. Benefiting from the introduction of iterative diffusion mechanism, the recommendation results not only cover the resources collected by the given user's direct neighbors but also cover the ones which are collected by his/her extended neighbors. Experimental results show that our method performs better in terms of precision and recall than other recommendation methods.