Real-time automatic tag recommendation
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
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
Learning optimal ranking with tensor factorization for tag recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning in efficient tag recommendation
Proceedings of the fourth ACM conference on Recommender systems
Personalized PageRank vectors for tag recommendations: inside FolkRank
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
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Real-world tagging datasets have a large proportion of new/unseen documents. Few approaches for recommending tags to a user for a document address this new item problem, concentrating instead on artificially created post-core datasets where it is guaranteed that the user as well as the document of each test post is known to the system and already has some tags assigned to it. In order to recommend tags for unseen documents, approaches are required which model documents not only based on the tags assigned to it in the past (if any), but also the content. The focus of my research is on utilising the content of documents in order to address the new item problem in tag recommendation. I apply this methodology first to simple baseline tag recommenders and then the more advanced tag recommendation algorithm FolkRank. One of my main contributions is a novel adaptation to the FolkRank graph model to use multiple word nodes instead of a single document node to represent each document. This enables FolkRank to recommend tags for unseen documents and makes it applicable to full real-world tagging datasets, addressing the new item problem in tag recommendation.