Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Modern Information Retrieval
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Learning to rank at query-time using association rules
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
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
Combating spam in tagging systems: An evaluation
ACM Transactions on the Web (TWEB)
Personalized, interactive tag recommendation for flickr
Proceedings of the 2008 ACM conference on Recommender systems
Proceedings of the 18th international conference on World wide web
Learning to recommend with social trust ensemble
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Automatic video tagging using content redundancy
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
Latent dirichlet allocation for tag recommendation
Proceedings of the third 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
Pairwise interaction tensor factorization for personalized tag recommendation
Proceedings of the third ACM international conference on Web search and data mining
Demand-driven tag recommendation
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
GreenMeter: a tool for assessing the quality and recommending tags for web 2.0 applications
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
ICWE'12 Proceedings of the 12th international conference on Web Engineering
Automatic query expansion based on tag recommendation
Proceedings of the 21st ACM international conference on Information and knowledge management
Assessing the quality of textual features in social media
Information Processing and Management: an International Journal
SpaDeS: Detecting spammers at the source network
Computer Networks: The International Journal of Computer and Telecommunications Networking
Exploiting novelty and diversity in tag recommendation
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Topic diversity in tag recommendation
Proceedings of the 7th ACM conference on Recommender systems
On combining text-based and link-based similarity measures for scientific papers
Proceedings of the 2013 Research in Adaptive and Convergent Systems
Measuring and addressing the impact of cold start on associative tag recommenders
Proceedings of the 19th Brazilian symposium on Multimedia and the web
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This work addresses the task of recommending relevant tags to a target object by jointly exploiting three dimensions of the problem: (i) term co-occurrence with tags pre-assigned to the target object, (ii) terms extracted from multiple textual features, and (iii) several metrics of tag relevance. In particular, we propose several new heuristic methods, which extend state-of-the-art strategies by including new metrics that try to capture how accurately a candidate term describes the object's content. We also exploit two learning-to-rank (L2R) techniques, namely RankSVM and Genetic Programming, for the task of generating ranking functions that combine multiple metrics to accurately estimate the relevance of a tag to a given object. We evaluate all proposed methods in various scenarios for three popular Web 2.0 applications, namely, LastFM, YouTube and YahooVideo. We found that our new heuristics greatly outperform the methods on which they are based, producing gains in precision of up to 181%, as well as another state-of-the-art technique, with improvements in precision of up to 40% over the best baseline in any scenario. Further improvements can also be achieved with the new L2R strategies, which have the additional advantage of being quite flexible and extensible to exploit other aspects of the tag recommendation problem.