In this paper, we propose a novel tag-based recommender system called PLIERS, which relies on the assumption that users are mainly interested in items and tags with similar popularity to those they already own. PLIERS is aimed at reaching 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 state-of-the-art solutions in terms of personalization, relevance, and novelty of recommendations.

翻译：本文提出一种名为PLIERS的新型标签推荐系统，其核心假设是用户主要对与其已拥有内容具有相似流行度的标签和项目感兴趣。PLIERS旨在实现算法复杂度与推荐项目个性化水平之间的良好平衡。为评估PLIERS，我们在真实在线社交网络数据集上进行了一系列实验，结果表明该系统在推荐结果的个性化、相关性和新颖性方面均优于现有最优解决方案。