Tag-aware recommender systems by fusion of collaborative filtering algorithms
Proceedings of the 2008 ACM symposium on Applied computing
Social ranking: uncovering relevant content using tag-based recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
Integrating tags in a semantic content-based recommender
Proceedings of the 2008 ACM conference on Recommender systems
Reasonable tag-based collaborative filtering for social tagging systems
Proceedings of the 2nd ACM workshop on Information credibility on the web
A hybrid approach to item recommendation in folksonomies
Proceedings of the WSDM '09 Workshop on Exploiting Semantic Annotations in Information Retrieval
On social networks and collaborative recommendation
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Topic-based recommendations for enterprise 2.0 resource sharing platforms
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part I
Who tags what?: an analysis framework
Proceedings of the VLDB Endowment
Exploratory mining of collaborative social content
Proceedings of the 2013 Sigmod/PODS Ph.D. symposium on PhD symposium
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Collaborative tagging has become a very popular way to share, annotate, and discover online resources in Web 2.0. Yet as the number of resources in Collaborative tagging system grows over time, sifting through the large amounts of resources and finding the right resources to recommend to the right user is becoming a challenging problem. In this paper, we investigate a probabilistic generative model for collaborative tagging, explore the implicit semantic connections in the sparse and noisy information space of heterogeneous users and unsupervised tagging. First, a modified Latent Dirichlet Allocation (LDA) model is used to cluster the tags and users simultaneously. The generalization of resource description and user could alleviate the tag noise and data sparseness of recommendation effectively. And then, considering that topic-based recommendation only takes the users' global interest into consideration without the capability of distinguishing users' interest in detail, we combine the global interests with the individual interest and community interest. Experimental results demonstrate the topic-based personalized recommendation method, which integrate both the commonality factor among users and the specialties of individuals, could alleviate data sparsity and provide a more flexible and effective recommendation than previous methods.