Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Personalized recommendation in social tagging systems using hierarchical clustering
Proceedings of the 2008 ACM conference on Recommender systems
The impact of ambiguity and redundancy on tag recommendation in folksonomies
Proceedings of the third ACM conference on Recommender systems
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Information retrieval in folksonomies: search and ranking
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
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Collaborative tagging applications or folksonomies allow internet users to annotate resources with personalized tags. However, freedom afforded users come at a cost: an uncontrolled vocabulary can result in tag ambiguity hindering navigation. Ambiguity can mislead users as they search for relevant resources. Recommenders that avoid ambiguous tags may be penalized by standard utility metrics for not promoting such tags. To provide effectiveness of tag recommendation in term of accuracy and coverage, we propose a new method for tag recommendation approach based on social semantic web to overcome ambiguity and presenting users tags and resources that correspond more closely. This method is a combination of semantic content analysis methods and social network analysis methods.