Tag Recommendation Datasets

Associative Tag Recommendation Exploiting Multiple Textual Features

Fabiano Belem, Eder Martins, Jussara M. Almeida Marcos Goncalves 
In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, July. 2011

Abstract

This work addresses the task of recommending relevant tags to a target object by jointly exploiting three dimen- sions of the problem: (i) term co-occurrence with tags preassigned to the target object, (ii) terms extracted from mul- tiple textual features, and (iii) several metrics of tag relevance. In particular, we propose several new heuristic meth- ods, which extend previous, highly effective and efficient, 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 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. Some further improvements can also be achieved, in some scenarios, with the new learning-to-rank based strategies, which have the additional advantage of being quite flexible and easily extensible to exploit other aspects of the tag recommendation problem.

Bibtex Citation

@inproceedings{belem@sigir11,
    author = {Fabiano Bel\'em and Eder Martins and Jussara Almeida and Marcos Gon\c{c}alves},
    title = {Associative Tag Recommendation Exploiting Multiple Textual Features},
    booktitle = {{Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval (SIGIR'11)}},
    month = {{July}},
    year = {2011}
}