Communications of the ACM
Getting to know you: learning new user preferences in recommender systems
Proceedings of the 7th international conference on Intelligent user interfaces
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
Evaluating WordNet-based Measures of Lexical Semantic Relatedness
Computational Linguistics
The Benefit of Using Tag-Based Profiles
LA-WEB '07 Proceedings of the 2007 Latin American Web Conference
Tag-aware recommender systems by fusion of collaborative filtering algorithms
Proceedings of the 2008 ACM symposium on Applied computing
Detecting innovative topics based on user-interest ontology
Web Semantics: Science, Services and Agents on the World Wide Web
Improving search in social networks by agent based mining
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Categorising social tags to improve folksonomy-based recommendations
Web Semantics: Science, Services and Agents on the World Wide Web
Analyzing user modeling on twitter for personalized news recommendations
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
Topic-based ranking in Folksonomy via probabilistic model
Artificial Intelligence Review
TOAST: a topic-oriented tag-based recommender system
WISE'11 Proceedings of the 12th international conference on Web information system engineering
Personalized topic-based tag recommendation
Neurocomputing
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Tag-based recommendation systems aim to improve the search experience of the end users. However, due to different backgrounds of the end users, descriptions of the same resources may be totally different in particle size and degree of specialization, which raises the question of how to tackle the growing discrepancy of public taxonomies (Folksonomy) in the social networks. In line with this, WordNet-based similarity is used to obtain semantic distance between tags and topic categories in order to reduce the divergence of tags. This in turn improves the search accuracy. The Bayesian reasoning is introduced to infer users' preferences through mining users' comments towards particular categories. Users' interaction behavior, which may facilitate preference estimation, is considered as well to enhance search efficiency. A series of experiments are conducted based on Flickr and Delicious datasets. The results show that the proposed recommendation algorithm can effectively improve search precision and provide a greater level of user satisfaction.