Self-Optimizing a Clustering-based Tag Recommender for Social Bookmarking Systems

In this paper, we propose and evaluate a self-optimization strategy for a clustering-based tag recommendation system. For tag recommendation, we use an efficient discriminative clustering approach. To develop our self-optimization strategy for this tag recommendation approach, we empirically investigate when and how to update the tag recommender with minimum human intervention. We present a nonlinear optimization model whose solution yields the clustering parameters that maximize the recommendation accuracy within an administrator specified time window. Evaluation on ``BibSonomy'' data produces promising results. For example, by using our self-optimization strategy a 6\% increase in average F1 score is achieved when the administrator allows \emph{up to} 2\% drop in average F1 score in the last one thousand recommendations.