Socially-Aware Recommender Systems Mitigate Opinion Clusterization
This addresses the issue of opinion polarization for users and creators in online platforms, though it appears incremental as it builds on existing social-aware methods.
The paper tackles the problem of filter bubbles and opinion polarization in recommender systems by developing a social network-aware system that strategically uses social network topology to promote diversification, showing that opinion clusterization is positively correlated with the influence of recommended content on user opinions.
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.