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Effect of recommending users and opinions on the network connectivity and idea generation process

Sriniwas Pandey, Hiroki Sayama

TL;DR

This paper investigates how recommender systems shape the interaction between individual traits (notably homophily and openness to novelty) and social-network dynamics, with a focus on both network fragmentation and the diversity of newly generated ideas. It extends a baseline opinion-dynamics model to include a 15-dimensional idea state and multiple recommendation strategies, enabling analysis of exposure-driven effects on network structure and idea generation. Key findings show that opinion-based recommendations can reduce fragmentation and foster locally diverse yet cohesive communities, while user-based strategies may amplify echo-chamber effects depending on underlying network parameters. The work provides design guidance for recommender systems in online platforms, highlighting the need to balance exploration of diverse opinions with cohesion to mitigate echo chambers and biased filtering.

Abstract

The growing reliance on online services underscores the crucial role of recommendation systems, especially on social media platforms seeking increased user engagement. 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. Additionally, the research examines the impact of recommendation systems on the diversity of newly generated ideas, shedding light on the challenges and opportunities in designing effective systems that balance the exploration of new ideas with the risk of reinforcing biases or filtering valuable, unconventional concepts.

Effect of recommending users and opinions on the network connectivity and idea generation process

TL;DR

This paper investigates how recommender systems shape the interaction between individual traits (notably homophily and openness to novelty) and social-network dynamics, with a focus on both network fragmentation and the diversity of newly generated ideas. It extends a baseline opinion-dynamics model to include a 15-dimensional idea state and multiple recommendation strategies, enabling analysis of exposure-driven effects on network structure and idea generation. Key findings show that opinion-based recommendations can reduce fragmentation and foster locally diverse yet cohesive communities, while user-based strategies may amplify echo-chamber effects depending on underlying network parameters. The work provides design guidance for recommender systems in online platforms, highlighting the need to balance exploration of diverse opinions with cohesion to mitigate echo chambers and biased filtering.

Abstract

The growing reliance on online services underscores the crucial role of recommendation systems, especially on social media platforms seeking increased user engagement. 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. Additionally, the research examines the impact of recommendation systems on the diversity of newly generated ideas, shedding light on the challenges and opportunities in designing effective systems that balance the exploration of new ideas with the risk of reinforcing biases or filtering valuable, unconventional concepts.
Paper Structure (12 sections, 6 equations, 4 figures)

This paper contains 12 sections, 6 equations, 4 figures.

Table of Contents

  1. Introduction
  2. Model
  3. Existing model
  4. Updated model
  5. Recommendation strategies
  6. Experiments
  7. Network dynamics
  8. Idea generation process
  9. Results
  10. Effect of recommendation strategies on network dynamics
  11. Effect of recommendation strategies on idea generation process
  12. Conclusion

Figures (4)

  • Figure 1: Violin plots representing distribution of network modularity for different recommendation strategies and parameter configurations: Each subfigure represents mudularities for the parameter setting described in the caption of subfigure. Modularity is calculated according to the partitions obtained using the Louvain community detection method. In all the cases except (d) when homophily is low, attention to novelty is high and initial edge weights are distributed according to uniform random distribution, opinion recommendation has significantly lower modularity.
  • Figure 2: Violin plots representing distribution of standard deviation of average community idea state for different recommendation strategies and parameter configurations: Each subfigure represents mudularities for the parameter setting described in the caption of subfigure. In all the cases except (d) when homophily is low, attention to novelty is high and initial edge weights are distributed according to uniform random distribution, opinion recommendation has significantly lower standard deviation representing closeness of different communities in terms of their mean idea state.
  • Figure 3: Violin plots representing distribution of opinion eccentricity for different recommendation strategies: Each subfigure represents distribution of eccentricity for the parameter setting described in the caption of the subfigure. Except the case of low homophily and initial weights following uniform random distribution, eccentricity is significantly high when opinion recommendation strategy is employed. Heatmap in each subfigure shows the significance level of difference.
  • Figure :