Online social networks (OSNs) allow users to generate items and tag or rate them in order to help others in the identification of useful content. In this paper, we propose a novel tag-based recommender system called PLIERS, able to identify useful contents based on users' interests. It relies on the assumption that users are mainly interested in items and tags with similar popularity to those they already own. It reaches a good tradeoff between algorithmic complexity and the level of personalization of recommended items. To evaluate PLIERS, we performed a set of experiments on real OSN datasets, demonstrating that it outperforms the state-of-the-art solutions in terms of personalization, relevance, and novelty of recommendations.
