Exploiting novelty and diversity in tag recommendation

Abstract

The design and evaluation of tag recommendation methods have focused only on relevance. However, other aspects such as novelty and diversity may be as important to evaluate the usefulness of the recommendations. In this work, we define these two aspects in the context of tag recommendation and propose a novel recommendation strategy that considers them jointly with relevance. This strategy extends a state-of-the-art method based on Genetic Programming to include novelty and diversity metrics both as attributes and as part of the objective function. We evaluate the proposed strategy using data collected from 3 popular Web 2.0 applications: LastFM, YouTube and YahooVideo. Our experiments show that our strategy outperforms the state-of-the-art alternative in terms of novelty and diversity, without harming relevance. © 2013 Springer-Verlag.