Improving Tag Recommendation using Few Associations

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 re-search has shown that accurate recommendation can be achieved by us-ing 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 accu-racy 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 off-the-shelf pattern selection method based on the Minimum Description Length principle. Experi-ments on data from Delicious, LastFM and YouTube show that our pro-posed methodology works well: applying pattern selection gives a very favourable trade-off between runtime and recommendation quality.