NDLI: Associative tag recommendation exploiting multiple textual features

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Associative tag recommendation exploiting multiple textual features

Content Provider	ACM Digital Library
Author	Belém, Fabiano Gonçalves, Marcos Martins, Eder Almeida, Jussara Pontes, Tatiana
Abstract	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.
Starting Page	1033
Ending Page	1042
Page Count	10
File Format	PDF
ISBN	9781450307574
DOI	10.1145/2009916.2010053
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-07-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Tag recommendation Relevance metrics
Content Type	Text
Resource Type	Article

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