Modern Information Retrieval
Being accurate is not enough: how accuracy metrics have hurt recommender systems
CHI '06 Extended Abstracts on Human Factors in Computing Systems
MultiTube--Where Web 2.0 and Multimedia Could Meet
IEEE MultiMedia
Flickr tag recommendation based on collective knowledge
Proceedings of the 17th international conference on World Wide Web
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Personalized, interactive tag recommendation for flickr
Proceedings of the 2008 ACM conference on Recommender systems
Evidence of quality of textual features on the web 2.0
Proceedings of the 18th ACM conference on Information and knowledge management
Demand-driven tag recommendation
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Rank and relevance in novelty and diversity metrics for recommender systems
Proceedings of the fifth ACM conference on Recommender systems
Assessing the quality of textual features in social media
Information Processing and Management: an International Journal
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Tag recommendation methods have mostly focused on maximizing relevance, but other aspects may be as important for recommendation usefulness. We here define novelty and diversity for tag recommendation, and propose two new recommendation strategies that consider these aspects jointly with relevance. We evaluate the proposed strategies using real datasets from 3 popular Web 2.0 applications, achieving gains over the state-of-the-art of up to 21% in relevance, 45% in novelty and 2.5\% in diversity.