Content- and Graph-based Tag Recommendation: Two Variations
ECML PKDD Discovery Challenge 2009 (DC09), 497, page 189--199. Bled, Slovenia, CEUR Workshop Proceedings, (September 2009)Abstract
We describe two variants of our approach to tackle the task 1 & 2 of the ECML PKDD Discovery Challenge 2009 where each contenter had to identify up to 5 tags for each resource of a given set of either bibtex-like references to publications or bookmarks. The quality of the results was measured against the tags that users of the data source (www.bibsonomy.org) had originally assigned to the resources (F1 measure). In our approach, we either generate tags (from the content of the given resource data or after crawling additional resources) or we request tags from tagging services. We call each of this tag sources a tag recommender. We then combine the results of the tag recommenders based on weighting factors. The weighting factors are determined experimentally by comparing generated and expected tags based on the available training data. This general idea is also used for the graph-based approach required to solve task 2. Here again, the final tag recommendations are computed from the individual results of the different tag-recommending algorithms. In the preliminary result list, we ranked second for task 1 (Group 2) and nineth for task 2 (Group 1).
