Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
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
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
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This work addresses the task of recommending high quality tags by exploiting not only previously assigned tags, but also terms extracted from other textual features (e.g., title and description) associated with the target object.To estimate the quality of a candidate tag recommendation, we use several metrics related to both tag co-occurrence and information quality. We also propose a heuristic function to combine the metrics to produce a final ranking of the recommended tags. We evaluate our heuristic function in various scenarios, for three popular Web 2.0 applications. Our experimental results indicate that our heuristic function significantly outperforms two state-of-the-art tag recommendation algorithms.