Information retrieval using a singular value decomposition model of latent semantic structure
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Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
A Multilinear Singular Value Decomposition
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Evaluation of Item-Based Top-N Recommendation Algorithms
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Evaluating collaborative filtering recommender systems
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ACM Transactions on Information Systems (TOIS)
CubeSVD: a novel approach to personalized Web search
WWW '05 Proceedings of the 14th international conference on World Wide Web
The complex dynamics of collaborative tagging
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A Tensor Approximation Approach to Dimensionality Reduction
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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
Cubic analysis of social bookmarking for personalized recommendation
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A hybrid approach to item recommendation in folksonomies
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Learning optimal ranking with tensor factorization for tag recommendation
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Cross-tagging for personalized open social networking
Proceedings of the 20th ACM conference on Hypertext and hypermedia
Latent dirichlet allocation for tag recommendation
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TagiCoFi: tag informed collaborative filtering
Proceedings of the third ACM conference on Recommender systems
The 3A contextual ranking system: simultaneously recommending actors, assets, and group activities
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I tag, you tag: translating tags for advanced user models
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Artificial Intelligence Review
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ACM Transactions on Information Systems (TOIS)
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Hybrid tag recommendation for social annotation systems
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Automatic tag recommendation algorithms for social recommender systems
ACM Transactions on the Web (TWEB)
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The VLDB Journal — The International Journal on Very Large Data Bases
Low-order tensor decompositions for social tagging recommendation
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Post-based collaborative filtering for personalized tag recommendation
Proceedings of the 2011 iConference
Tag recommendation based on Bayesian principle
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
Effective hybrid recommendation combining users-searches correlations using tensors
APWeb'11 Proceedings of the 13th Asia-Pacific web conference on Web technologies and applications
Tag-based resource recommendation in social annotation applications
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
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Unsupervised auto-tagging for learning object enrichment
EC-TEL'11 Proceedings of the 6th European conference on Technology enhanced learning: towards ubiquitous learning
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Neurocomputing
Resource recommendation in social annotation systems: A linear-weighted hybrid approach
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Link prediction on evolving data using tensor factorization
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
Towards mobile intelligence: Learning from GPS history data for collaborative recommendation
Artificial Intelligence
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Tripartite community structure in social bookmarking data
The New Review of Hypermedia and Multimedia - Special issue on Social Linking and Hypermedia
Folksonomy-Based Term Extraction for Word Cloud Generation
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Latent Geospatial Semantics of Social Media
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Mining contextual movie similarity with matrix factorization for context-aware recommendation
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Social collaborative retrieval
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A framework for tag-aware recommender systems
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Hybrid recommendation approaches for multi-criteria collaborative filtering
Expert Systems with Applications: An International Journal
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Social tagging is the process by which many users add metadata in the form of keywords, to annotate and categorize information items (songs, pictures, web links, products etc.). Collaborative tagging systems recommend tags to users based on what tags other users have used for the same items, aiming to develop a common consensus about which tags best describe an item. However, they fail to provide appropriate tag recommendations, because: (i) users may have different interests for an information item and (ii) information items may have multiple facets. In contrast to the current tag 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 is represented by a 3-order tensor, on which latent semantic analysis and dimensionality reduction is performed using the Higher Order Singular Value Decomposition (HOSVD) technique. We perform experimental comparison of the proposed method against two state-of-the-art tag recommendations algorithms with two real data sets (Last.fm and BibSonomy). Our results show significant improvements in terms of effectiveness measured through recall/precision.