PLIERS: a Popularity-Based Recommender System for Content Dissemination in Online Social Networks
This addresses content dissemination for users in online social networks, but appears incremental as it builds on existing tag-based and popularity-based methods.
The paper tackles the problem of content recommendation in online social networks by proposing PLIERS, a tag-based system that assumes user interest aligns with item and tag popularity, achieving a trade-off between complexity and personalization. Experiments on real datasets show it outperforms state-of-the-art solutions in personalization, relevance, and novelty.
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.