Breaking the Filter Bubble: A Semantic Pareto-DQN Framework for Multi-Objective Recommendation
arXiv:2606.24042v1 Announce Type: new Abstract: Recommender systems often induce filter bubbles and semantic homogenization by monolithically optimizing for immediate user engagement. Standard single-objective models, including traditional Deep Q-Networks, are ill-equipped to navigate the trade-offs between platform retention and critical societal values like information diversity and provider fairness. To address these limitations, we introduce a multi-objective reinforcement learning framework that formalizes recommendation as a semantic multi-objective Markov decision process. By integratin
The increasing public and regulatory scrutiny on the societal impact of AI systems, particularly recommender systems, is driving this research to develop more ethically aligned algorithms.
This work directly addresses the critical issue of 'filter bubbles' and the homogenization of information, which has significant implications for public discourse, social cohesion, and regulatory policy.
Recommender systems may evolve beyond purely engagement-driven metrics to incorporate objectives like diversity and fairness, potentially altering content consumption patterns and platform utility.
- · Users seeking diverse information
- · News and content providers outside mainstream algorithms
- · Regulators and policymakers focused on platform responsibility
- · Researchers in multi-objective reinforcement learning
- · Platforms solely optimizing for short-term engagement
- · Advertisers relying on highly targeted, narrow content feeds
Recommendation algorithms will adopt more complex, multi-objective optimization strategies.
This could lead to platforms displaying a wider variety of content to users, potentially impacting user engagement metrics.
Enhanced information diversity could foster more nuanced public opinions and reduce societal polarization over the long term.
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Read at arXiv cs.AI