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Socially-Aware Recommender Systems Mitigate Opinion Clusterization

Lukas Schüepp, Carmen Amo Alonso, Florian Dörfler, Giulia De Pasquale

TL;DR

The paper tackles the challenge of opinion polarization and filter bubbles in recommender systems by introducing a socially-aware RS that leverages users' social network structure. It formulates a dynamic, multi-agent model based on a multi-topic Friedkin–Johnsen framework to capture the evolving opinions of users and creators under RS interactions, with a $d$-hop social influence mechanism guiding recommendations. Key contributions include the formalization of satisfaction and clusterization metrics within this dynamic setting, the proposal of a socially-aware RS that mimics social networks through $d$-hop neighborhood averages, and theoretical and empirical demonstrations that incorporating social structure can mitigate clusterization while balancing personalization. The findings suggest practical design principles for RS in real-world platforms to reduce polarization and echo-chamber effects without sacrificing user engagement, supported by synthetic and real-network experiments. Let $\text{sat}(u_i^T,c_j) = -\dfrac{1}{T}\sum_{t=0}^{T-1} \|u_i^t - c_j^t\|_2$ and $\text{cl}(\mathcal{U}^t) = \dfrac{1}{N}\sum_i s(u_i^t)$ with $s(u_i^t)$ the silhouette, and $r_i^t = r(u_i^t)$ guiding the RS via $c_j^t \sim \mathcal{R}(u_i^t)$. The approach demonstrates that music to policy can be found by balancing local personalization against global opinion diversity through network-aware control of recommendations.

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.

Socially-Aware Recommender Systems Mitigate Opinion Clusterization

TL;DR

The paper tackles the challenge of opinion polarization and filter bubbles in recommender systems by introducing a socially-aware RS that leverages users' social network structure. It formulates a dynamic, multi-agent model based on a multi-topic Friedkin–Johnsen framework to capture the evolving opinions of users and creators under RS interactions, with a -hop social influence mechanism guiding recommendations. Key contributions include the formalization of satisfaction and clusterization metrics within this dynamic setting, the proposal of a socially-aware RS that mimics social networks through -hop neighborhood averages, and theoretical and empirical demonstrations that incorporating social structure can mitigate clusterization while balancing personalization. The findings suggest practical design principles for RS in real-world platforms to reduce polarization and echo-chamber effects without sacrificing user engagement, supported by synthetic and real-network experiments. Let and with the silhouette, and guiding the RS via . The approach demonstrates that music to policy can be found by balancing local personalization against global opinion diversity through network-aware control of recommendations.

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.
Paper Structure (35 sections, 3 theorems, 22 equations, 9 figures, 3 tables)

This paper contains 35 sections, 3 theorems, 22 equations, 9 figures, 3 tables.

Table of Contents

  1. introduction
  2. Related Work
  3. Problem Setup
  4. User-user interaction
  5. Creator-user interaction
  6. Recommender System
  7. Satisfaction.
  8. Clusterization.
  9. Recommender System Design
  10. Recommendation Strategies for Satisfaction and Clusterization
  11. Social Interactions mitigate the effect of Recommendations
  12. RS that Maximize Satisfaction increase Clusterization
  13. RS that reduce Clusterization decrease Satisfaction
  14. A Socially-Aware Recommender System Mimics the Social Network
  15. Experiments
  16. ...and 20 more sections

Key Result

Theorem 1

Given the system with dynamics in equation eqn:simple_dynamics, the users' opinions reaches a steady state, i.e., $\exists \mathbf u^*\in\mathbb R^N$ such that $\lim_{t\rightarrow \infty}\ \mathbf{u}^t = \mathbf u^*$. Moreover, the influence of the social network ($A$) towards each user $i\in\{1,\do

Figures (9)

  • Figure 1: Overview of the dynamic framework described in equation equation \ref{['eqn:dynamics']}. Users $\mathcal{U}$ influence each other via dynamics $\textcolor{green}{f(\cdot)}$, and are influenced by creators via $\textcolor{pink}{h^t(\cdot)}$, mediated by $\textcolor{purple}{\mathcal{R}}$. Content creators have internal opinion dynamics $\textcolor{orange}{q(\cdot)}$, and are influenced by users via $\textcolor{blue}{p^t(\cdot)}$.
  • Figure 2: Snapshots of the opinion environment simulated with a localized region (i) $d=0$, first row, (ii) $d=6$, second row (iii) $d=3$, third row. $\times$ denotes the creator, $\bullet$ the users respectively.
  • Figure 3: Global clusterization and global user satisfaction plotted as $d$ varies after (a) 50 and (b) 500 timesteps. Clusterization thresh is set to $-0.5$, for which clusters are no longer distinguishable.
  • Figure 4: Snapshots after $t=20$ timesteps of the opinion environment simulated with a localized region (i) $d=0$, first row, (ii) $d=3$, second row. $\times$ denotes the creator, $\bullet$ the users respectively.
  • Figure 5: Global clusterization and global user satisfaction plotted as $d$ varies after (a) 50 and (b) 500 timesteps with more social interactions as opposed to \ref{['fig:varying_lambda']}. Clusterization thresh is set to $-0.5$, for which clusters are no longer distinguishable.
  • ...and 4 more figures

Theorems & Definitions (14)

  • Definition 1: Social Network
  • Definition 2: $d$-hop influencers
  • Definition 3
  • Definition 4: User Satisfaction
  • Definition 5: Global Satisfaction
  • Definition 6: User Silhouette
  • Definition 7: Global Clusterization
  • Theorem 1
  • Lemma 1
  • Corollary 1
  • ...and 4 more