Self-Optimizing a Clustering-based Tag Recommender for Social Bookmarking Systems
Malik Tahir Hassan, Asim Karim, Fahad Javed, Naveed Arshad · 2010
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 up to 2% drop in average F1 score in the last one thousand recommendations.