Flickr tag recommendation based on collective knowledge
Proceedings of the 17th international conference on World Wide Web
Tag Recommendations in Folksonomies
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Tag recommendations based on tensor dimensionality reduction
Proceedings of the 2008 ACM conference on Recommender systems
A random walk method for alleviating the sparsity problem in collaborative filtering
Proceedings of the 2008 ACM conference on Recommender systems
Learning optimal ranking with tensor factorization for tag recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
On social networks and collaborative recommendation
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
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
TagiCoFi: tag informed collaborative filtering
Proceedings of the third ACM conference on Recommender systems
IEEE Transactions on Knowledge and Data Engineering
Pairwise interaction tensor factorization for personalized tag recommendation
Proceedings of the third ACM international conference on Web search and data mining
Connecting users and items with weighted tags for personalized item recommendations
Proceedings of the 21st ACM conference on Hypertext and hypermedia
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
Personalized recommender system based on item taxonomy and folksonomy
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Supervised random walks: predicting and recommending links in social networks
Proceedings of the fourth ACM international conference on Web search and data mining
Improving Recommender Systems by Incorporating Social Contextual Information
ACM Transactions on Information Systems (TOIS)
Semi-supervised ranking on very large graphs with rich metadata
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Integrating semantic relatedness and words' intrinsic features for keyword extraction
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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A social tagging system provides users an effective way to collaboratively annotate and organize items with their own tags. A social tagging system contains heterogeneous information like users' tagging behaviors, social networks, tag semantics and item profiles. All the heterogeneous information helps alleviate the cold start problem due to data sparsity. In this paper, we model a social tagging system as a multi-type graph. To learn the weights of different types of nodes and edges, we propose an optimization framework, called OptRank. OptRank can be characterized as follows:(1) Edges and nodes are represented by features. Different types of edges and nodes have different set of features. (2) OptRank learns the best feature weights by maximizing the average AUC (Area Under the ROC Curve) of the tag recommender. We conducted experiments on two publicly available datasets, i.e., Delicious and Last.fm. Experimental results show that: (1) OptRank outperforms the existing graph based methods when only (user, tag, item) relation is available. (2) OptRank successfully improves the results by incorporating social network, tag semantics and item profiles.