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Demand-Driven Tag Recommendation
| Citations: | 4 - 1 self |
BibTeX
@MISC{Menezes_demand-driventag,
author = {Guilherme Vale Menezes and Jussara M. Almeida and Fabiano Belém and Marcos André and Anísio Lacerda and Edleno Silva De Moura and Nivio Ziviani},
title = {Demand-Driven Tag Recommendation},
year = {}
}
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Abstract
Abstract. Collaborative tagging allows users to assign arbitrary keywords (or tags) describing the content of objects, which facilitates navigation and improves searching without dependence on pre-configured categories. In large-scale tagbased systems, tag recommendation services can assist a user in the assignment of tags to objects and help consolidate the vocabulary of tags across users. A promising approach for tag recommendation is to exploit the co-occurrence of tags. However, these methods are challenged by the huge size of the tag vocabulary, either because (1) the computational complexity may increase exponentially with the number of tags or (2) the score associated with each tag may become distorted since different tags may operate in different scales and the scores are not directly comparable. In this paper we propose a novel method that recommends tags on a demand-driven basis according to an initial set of tags applied to an object. It reduces the space of possible solutions, so that its complexity increases polynomially with the size of the tag vocabulary. Further, the score of each tag is calibrated using an entropy minimization approach which corrects possible distortions and provides more precise recommendations. We conducted a systematic evaluation of the proposed method using three types of media: audio, bookmarks and video. The experimental results show that the proposed method is fast and boosts recommendation quality on different experimental scenarios. For instance, in the case of a popular audio site it provides improvements in precision (p@5) ranging from 6.4 % to 46.7 % (depending on the number of tags given as input), outperforming a recently proposed co-occurrence based tag recommendation method. 1
Keyphrases
demand-driven tag recommendation tag vocabulary large-scale tagbased system different scale possible solution initial set pre-configured category arbitrary keywords recommendation quality demand-driven basis precise recommendation complexity increase tag recommendation method tag recommendation entropy minimization approach huge size different tag systematic evaluation different experimental scenario tag recommendation service possible distortion computational complexity popular audio site promising approach novel method experimental result
