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Evidence record 673 · automatically gathered

Breaking the Filter Bubble: A Semantic Pareto-DQN Framework for Multi-Objective Recommendation

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 mu

Record details

Published: 23 June 2026
Source: arXiv
Category: Research
Topics: Bias & fairness
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (23 June 2026), “Breaking the Filter Bubble: A Semantic Pareto-DQN Framework for Multi-Objective Recommendation,” evidence record 673, https://ethics.ai/record/673 (originally published by arXiv).

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