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 integrating high-fidelity semantic embeddings with a Pareto-DQN agent, our architecture treats engagement, diversity, and fairness as distinct, non-aggregable reward signals, avoiding the pitfalls of static reward scalarization. Empirical evaluations on the MovieLens small dataset shows that our hypervolume based action selection disrupts the feedback loops responsible for semantic collapse. By sustaining high state-trajectory variance, the Pareto-DQN effectively maps the Pareto frontier, achieving gains in auxiliary societal objectives with only marginal impacts on engagement. This work provides a path toward intrinsically aligned, responsible recommender systems.
[2606.24042] Breaking the Filter Bubble: A Semantic Pareto-DQN Framework for Multi-Objective Recommendation
[Submitted on 23 Jun 2026]
Title:Breaking the Filter Bubble: A Semantic Pareto-DQN Framework for Multi-Objective Recommendation
View a PDF of the paper titled Breaking the Filter Bubble: A Semantic Pareto-DQN Framework for Multi-Objective Recommendation, by Cl\'audio L\'ucio Do Val Lopes and 2 other authors
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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 integrating high-fidelity semantic embeddings with a Pareto-DQN agent, our architecture treats engagement, diversity, and fairness as distinct, non-aggregable reward signals, avoiding the pitfalls of static reward scalarization. Empirical evaluations on the MovieLens small dataset shows that our hypervolume based action selection disrupts the feedback loops responsible for semantic collapse. By sustaining high state-trajectory variance, the Pareto-DQN effectively maps the Pareto frontier, achieving gains in auxiliary societal objectives with only marginal impacts on engagement. This work provides a path toward intrinsically aligned, responsible recommender systems.
Comments: IEEE International Conference on Responsible Artificial Intelligence (IRAI) - 2026
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.24042 [cs.AI]
(or arXiv:2606.24042v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.24042
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Claudio Lopes [view email] [v1] Tue, 23 Jun 2026 00:58:16 UTC (947 KB)
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