Spectral Clustering in Social-Tagging Systems
WISE '09 Proceedings of the 10th International Conference on Web Information Systems Engineering
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
Hybrid tag recommendation for social annotation systems
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Which photo groups should I choose? A comparative study of recommendation algorithms in Flickr
Journal of Information Science
Low-order tensor decompositions for social tagging recommendation
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A unified framework for recommendations based on quaternary semantic analysis
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ACM Transactions on Management Information Systems (TMIS)
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
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Expert Systems with Applications: An International Journal
A literature review and classification of recommender systems research
Expert Systems with Applications: An International Journal
RED'10 Proceedings of the Third international conference on Resource Discovery
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Improving tensor based recommenders with clustering
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
Concurrency and Computation: Practice & Experience
Mining contextual movie similarity with matrix factorization for context-aware recommendation
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context
A self-adapted method for the categorization of social resources
Expert Systems with Applications: An International Journal
ACM Transactions on Interactive Intelligent Systems (TiiS)
Making recommendations from multiple domains
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
A Random Walk Model for Item Recommendation in Social Tagging Systems
ACM Transactions on Management Information Systems (TMIS)
Social semantic query expansion
ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
Flickr group recommendation based on user-generated tags and social relations via topic model
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
Dynamic adaptation of numerical attributes in a user profile
Applied Intelligence
A framework for tag-aware recommender systems
Expert Systems with Applications: An International Journal
CooL-AgentSpeak: Endowing AgentSpeak-DL agents with plan exchange and ontology services
Web Intelligence and Agent Systems
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Social Tagging is the process by which many users add metadata in the form of keywords, to annotate and categorize items (songs, pictures, Web links, products, etc.). Social tagging systems (STSs) can provide three different types of recommendations: They can recommend 1) tags to users, based on what tags other users have used for the same items, 2) items to users, based on tags they have in common with other similar users, and 3) users with common social interest, based on common tags on similar items. However, users may have different interests for an item, and items may have multiple facets. In contrast to the current recommendation algorithms, our approach develops a unified framework to model the three types of entities that exist in a social tagging system: users, items, and tags. These data are modeled by a 3-order tensor, on which multiway latent semantic analysis and dimensionality reduction is performed using both the Higher Order Singular Value Decomposition (HOSVD) method and the Kernel-SVD smoothing technique. We perform experimental comparison of the proposed method against state-of-the-art recommendation algorithms with two real data sets (Last.fm and BibSonomy). Our results show significant improvements in terms of effectiveness measured through recall/precision.