Socially-Aware Recommender Systems Mitigate User-Creator Features Clusterization

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 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 users' social network in the recommender system design is crucial to mediate filter bubble effects while balancing content diversity with personalization. We show that a recommender system that greedly optimizes for user satisfaction leads to users' feature clusters formation. Ultimately, the proposed approach shows the power of socially-aware recommender systems in combating polarization and clusterization phenomena.