具有社会意识的推荐系统缓解意见聚类
Socially-Aware Recommender Systems Mitigate Opinion Clusterization
AI总结：
本文提出一种考虑用户社会网络的推荐系统，以缓解过滤气泡和意见极化问题。
摘要：
推荐系统通过将用户与创作者内容匹配以最大化参与度来塑造在线互动。创作者会根据用户偏好调整内容以提高受欢迎程度。同时，用户偏好受到推荐系统建议的内容和社会圈内共享内容的共同影响。这种反馈循环产生了用户、创作者和推荐算法之间的复杂互动，是过滤气泡和意见极化的主要原因。我们开发了一种社会网络-aware的推荐系统，该系统明确考虑了这种用户-创作者反馈互动，并战略性地利用用户自身社会网络的拓扑结构以促进多样性。我们的方法强调了在推荐系统设计中考虑和利用用户社会网络的重要性，以在平衡内容多样性和个性化的同时缓解过滤气泡效应。证明了意见聚类与推荐内容对用户意见影响呈正相关。最终，所提出的方法展示了社会意识推荐系统在对抗意见极化和聚类现象中的强大能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
