A parameter-free algorithm for an optimized tag recommendation list size

Tag recommendation is a major aspect of collaborative tagging systems. It aims to recommend suitable tags to a user for tagging an item. One of its main challenges is the effectiveness of its recommendations. Existing works focus on techniques for retrieving the most relevant tags to give beforehand, with a fixed number of tags in each recommended list. In this paper, we try to optimize the number of recommended tags in order to improve the efficiency of the recommendations. We propose a parameter-free algorithm for determining the optimal size of the recommended list. Thus we introduced some relevance measures to find the most relevant sublist from a given list of recommended tags. More precisely, we improve the quality of our recommendations by discarding some unsuitable tags and thus adjusting the list size. Our solution is an add-on one, which can be implemented on top of many kinds of tag recommenders. The experiments we did on five datasets, using four categories of tag recommenders, demonstrate the efficiency of our technique. For instance, the algorithm we propose outperforms the results of the task 2 of the ECML PKDD Discovery Challenge 2009 1. By using the same tag recommender than the winners of the contest, we reach a F1 measure of 0.366 while the latter got 0.356. Thus, our solution yields significant improvements on the lists obtained from the tag recommenders.