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.

翻译：推荐系统通过将用户与创作者内容进行匹配以最大化参与度，从而塑造在线互动。相应地，创作者会调整其内容以适应用户偏好并提升自身受欢迎程度。同时，用户的偏好在推荐系统所建议内容及其社交圈内分享内容的共同影响下不断演变。这一反馈循环在用户、创作者和推荐算法之间产生了复杂的相互作用，这是导致信息茧房和观点两极分化的关键原因。我们开发了一种社交网络感知的推荐系统，该系统明确考虑了这种用户-创作者反馈互动，并策略性地利用用户自身社交网络的拓扑结构来促进内容多样化。我们的方法强调了在推荐系统设计中考虑并利用用户社交网络对于调节信息茧房效应、同时平衡内容多样性与个性化至关重要。可证明的是，观点集群化与推荐内容对用户观点的影响力呈正相关。最终，所提出的方法展示了社交感知推荐系统在对抗观点两极分化和集群化现象方面的强大能力。