A probabilistic model of information retrieval: development and comparative experiments Part 2
Information Processing and Management: an International Journal
A Faster Katz Status Score Algorithm
Computational & Mathematical Organization Theory
Link prediction approach to collaborative filtering
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
The link-prediction problem for social networks
Journal of the American Society for Information Science and Technology
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Exploring folksonomy for personalized search
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Social ranking: uncovering relevant content using tag-based recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
Can all tags be used for search?
Proceedings of the 17th ACM conference on Information and knowledge management
Tag recommendations in social bookmarking systems
AI Communications
Proceedings of the 18th international conference on World wide web
Neighborhood-Based Tag Prediction
ESWC 2009 Heraklion Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications
Personalized tag recommendation using graph-based ranking on multi-type interrelated objects
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Latent dirichlet allocation for tag recommendation
Proceedings of the third ACM conference on Recommender systems
Music information retrieval using social tags and audio
IEEE Transactions on Multimedia - Special section on communities and media computing
I tag, you tag: translating tags for advanced user models
Proceedings of the third ACM international conference on Web search and data mining
Pairwise interaction tensor factorization for personalized tag recommendation
Proceedings of the third ACM international conference on Web search and data mining
Learning in efficient tag recommendation
Proceedings of the fourth ACM conference on Recommender systems
Music recommendation by unified hypergraph: combining social media information and music content
Proceedings of the international conference on Multimedia
Automatic tag recommendation algorithms for social recommender systems
ACM Transactions on the Web (TWEB)
Second workshop on information heterogeneity and fusion in recommender systems (HetRec2011)
Proceedings of the fifth ACM conference on Recommender systems
Information retrieval in folksonomies: search and ranking
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
A Tag-Based Hybrid Music Recommendation System Using Semantic Relations and Multi-domain Information
ICDMW '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops
Personalizing web search with folksonomy-based user and document profiles
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
A Probabilistic Model to Combine Tags and Acoustic Similarity for Music Retrieval
ACM Transactions on Information Systems (TOIS)
Finding the hidden gems: recommending untagged music
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Recommending tags with a model of human categorization
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Nowadays social tagging has become a popular way to annotate, search, navigate and discover online resources, in turn leading to the sheer amount of user-generated metadata. This paper addresses the problem of recommending suitable tags during folksonomy development from a graph-based perspective. The proposed approach adapts the Katz measure, a path-ensemble based proximity measure, for the use in social tagging systems. We model a folksonomy as a weighted, undirected tripartite graph. We then apply the Katz measure to this graph, and exploit it to provide tag recommendations for individual users. We evaluate our method on two real-world folksonomies collected from CiteULike and Last.fm. The experimental results demonstrate that the proposed method improves the recommendation performance and is effective for both active taggers and cold-start taggers compared to existing algorithms.