Socially-Aware Recommender Systems Mitigate Opinion Clusterization

• URL: http://arxiv.org/abs/2601.02412v1
• Date: Fri, 02 Jan 2026 16:54:05 GMT
• Title: Socially-Aware Recommender Systems Mitigate Opinion Clusterization
• Authors: Lukas Schüepp, Carmen Amo Alonso, Florian Dörfler, Giulia De Pasquale,
• Abstract summary: We argue that recommender systems shape online interactions by matching users with creators content to maximize engagement.<n>This feedback loop generates a complex interplay between users, creators, and recommender algorithms, which is the key cause of filter bubbles and opinion polarization.<n>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.
• Score: 7.513055111258215
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Tree of Preferences for Diversified Recommendation [54.183647833064136]
  We study diversified recommendation from a data-bias perspective.<n>Inspired by the outstanding performance of large language models (LLMs) in zero-shot inference leveraging world knowledge, we propose a novel approach.
  arXiv  Detail & Related papers  (2025-12-24T04:13:17Z)
• RecoWorld: Building Simulated Environments for Agentic Recommender Systems [55.979427290369216]
  We present RecoWorld, a blueprint for building simulated environments tailored to agentic recommender systems.<n>A user simulator reviews recommended items, updates its mindset, and when sensing potential user disengagement, generates reflective instructions.<n>The agentic recommender adapts its recommendations by incorporating these user instructions and reasoning traces, creating a dynamic feedback loop.
  arXiv  Detail & Related papers  (2025-09-12T16:44:34Z)
• RecGPT Technical Report [57.84251629878726]
  We propose RecGPT, a next-generation framework that places user intent at the center of the recommendation pipeline.<n> RecGPT integrates large language models into key stages of user interest mining, item retrieval, and explanation generation.<n>Online experiments demonstrate that RecGPT achieves consistent performance gains across stakeholders.
  arXiv  Detail & Related papers  (2025-07-30T17:55:06Z)
• Looking for Fairness in Recommender Systems [0.6216023343793144]
  We're in the process of building a recommender system to make content suggestions to users on social media.<n>A shared fairness concern across all three is the emergence of filter bubbles.<n>From the user's perspective, this is akin to manipulation.<n>From society's perspective, the potential consequences are far-reaching.
  arXiv  Detail & Related papers  (2025-07-16T13:53:02Z)
• Search-Based Interaction For Conversation Recommendation via Generative Reward Model Based Simulated User [117.82681846559909]
  Conversational recommendation systems (CRSs) use multi-turn interaction to capture user preferences and provide personalized recommendations.<n>We propose a generative reward model based simulated user, named GRSU, for automatic interaction with CRSs.
  arXiv  Detail & Related papers  (2025-04-29T06:37:30Z)
• The Feedback Loop Between Recommendation Systems and Reactive Users [6.660458629649826]
  We model the feedback loop between users' opinion dynamics and a recommendation system.<n>We show how reactive policies can help users effectively prevent or restrict undesirable opinion shifts.
  arXiv  Detail & Related papers  (2025-03-14T19:45:57Z)
• User Welfare Optimization in Recommender Systems with Competing Content Creators [65.25721571688369]
  In this study, we perform system-side user welfare optimization under a competitive game setting among content creators. We propose an algorithmic solution for the platform, which dynamically computes a sequence of weights for each user based on their satisfaction of the recommended content. These weights are then utilized to design mechanisms that adjust the recommendation policy or the post-recommendation rewards, thereby influencing creators' content production strategies.
  arXiv  Detail & Related papers  (2024-04-28T21:09:52Z)
• Effect of recommending users and opinions on the network connectivity and idea generation process [0.1843404256219181]
  This study investigates how recommendation systems influence the impact of personal behavioral traits on social network dynamics. It explores the interplay between homophily, users' openness to novel ideas, and recommendation-driven exposure to new opinions.
  arXiv  Detail & Related papers  (2024-01-29T19:22:24Z)
• The Impact of Recommendation Systems on Opinion Dynamics: Microscopic versus Macroscopic Effects [1.4180331276028664]
  We study the impact of recommendation systems on users, both from a microscopic (i.e., at the level of individual users) and a macroscopic perspective. Our analysis reveals that shifts in the opinions of individual users do not always align with shifts in the opinion distribution of the population.
  arXiv  Detail & Related papers  (2023-09-16T11:44:51Z)
• Breaking Feedback Loops in Recommender Systems with Causal Inference [99.22185950608838]
  Recent work has shown that feedback loops may compromise recommendation quality and homogenize user behavior. We propose the Causal Adjustment for Feedback Loops (CAFL), an algorithm that provably breaks feedback loops using causal inference. We show that CAFL improves recommendation quality when compared to prior correction methods.
  arXiv  Detail & Related papers  (2022-07-04T17:58:39Z)
• Echo Chambers in Collaborative Filtering Based Recommendation Systems [1.5140493624413542]
  We simulate the recommendations given by collaborative filtering algorithms on users in the MovieLens data set. We find that prolonged exposure to system-generated recommendations substantially decreases content diversity. Our work suggests that once these echo-chambers have been established, it is difficult for an individual user to break out by manipulating solely their own rating vector.
  arXiv  Detail & Related papers  (2020-11-08T02:35:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.