Paper Info
Title
Demand-driven tag recommendation
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 tag-based 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.
Year
DOI
Venue
2010
10.1007/978-3-642-15883-4_26
ECML/PKDD
Keywords
Field
DocType
tag recommendation,novel method,tag recommendation method,precise recommendation,different tag,demand-driven tag recommendation,tag recommendation service,different experimental scenario,tag vocabulary,recommendation quality,computational complexity
Data mining,Latent Dirichlet allocation,Information retrieval,Computer science,Exploit,Association rule learning,Ranging,Mean reciprocal rank,Vocabulary,Demand driven,Computational complexity theory
Conference
Volume
ISSN
ISBN
6322
0302-9743
3-642-15882-X
Citations 
PageRank 
References 
16
0.70
29
Authors
9
Name
Order
Citations
PageRank
Guilherme Vale Menezes1160.70
Jussara M. Almeida23044310.86
Fabiano Belém31339.01
Marcos André Gonçalves4201.15
Anísio Lacerda517216.18
Edleno Silva de Moura698875.44
Gisele L. Pappa734736.97
Adriano Veloso874954.37
Nivio Ziviani91598154.65