Socially-Aware Recommender Systems Mitigate Opinion Clusterization

TL;DR

This paper introduces a socially-aware recommender system that leverages social network topology to reduce opinion polarization and filter bubbles, balancing content diversity with personalization.

Contribution

It develops a novel social network-aware recommender system that explicitly models user-creator interactions and social influence to mitigate opinion clustering.

Findings

The approach effectively reduces opinion polarization.

Exploiting social network topology enhances content diversification.

The method balances personalization with diversity.

Abstract

Recommender systems shape online interactions by matching users with creators content to maximize engagement. Creators, in turn, adapt their content to align with users preferences and enhance their popularity. At the same time, users preferences evolve under the influence of both suggested content from the recommender system and content shared within their social circles. This feedback loop generates a complex interplay between users, creators, and recommender algorithms, which is the key cause of filter bubbles and opinion polarization. We develop a social network-aware recommender system that explicitly accounts for this user-creators feedback interaction and strategically exploits the topology of the user's own social network to promote diversification. Our approach highlights how accounting for and exploiting user's social network in the recommender system design is crucial to mediate filter bubble effects while balancing content diversity with personalization. Provably, opinion clusterization is positively correlated with the influence of recommended content on user opinions. Ultimately, the proposed approach shows the power of socially-aware recommender systems in combating opinion polarization and clusterization phenomena.