PLIERS: a Popularity-Based Recommender System for Content Dissemination in Online Social Networks

TL;DR

This paper introduces PLIERS, a tag-based recommender system for online social networks that balances personalization and complexity by leveraging item and tag popularity, demonstrating superior performance over existing methods.

Contribution

The paper presents PLIERS, a novel popularity-based recommendation algorithm that improves personalization and relevance in social network content dissemination.

Findings

PLIES outperforms state-of-the-art solutions in personalization.

PLIES provides more relevant and novel recommendations.

The system effectively balances complexity and personalization.

Abstract

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.