Leeuwen, Matthijs van and Puspitaningrum, Diah (2014) IMPROVING TAG RECOMMENDATION USING FEW ASSOCIATIONS. In: Advances in Intelligent DataAnalysis XI, 25-27 Oktober 2012, Helsinki, Finland.
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Abstract
Collaborative tagging services allow users to freely assign
tags to resources. As the large majority of users enters only very few tags, good tag recommendation can vastly improve the usability of tags for techniques such as searching, indexing, and clustering. Previous research
has shown that accurate recommendation can be achieved by using conditional probabilities computed from tag associations. The main problem, however, is that enormous amounts of associations are needed for optimal recommendation.We argue and demonstrate that pattern selection techniques can improve tag recommendation by giving a very favourable balance between accuracy
and computational demand. That is, few associations are chosen to act as information source for recommendation, providing high-quality recommendation and good scalability at the same time. We provide a proof-of-concept using an o�-the-shelf pattern selection method based on the Minimum Description Length principle. Experiments on data from Delicious, LastFM and YouTube show that our proposed
methodology works well: applying pattern selection gives a very favourable trade-o� between runtime and recommendation quality
Item Type: | Conference or Workshop Item (Paper) |
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Subjects: | T Technology > T Technology (General) |
Divisions: | Faculty of Engineering > Department of Informatics Engineering |
Depositing User: | Users 30 not found. |
Date Deposited: | 27 Mar 2014 04:39 |
Last Modified: | 27 Mar 2014 04:39 |
URI: | https://repository.unib.ac.id/id/eprint/6901 |