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

Computer Science > Artificial Intelligence [Submitted on 23 Jun 2026] Breaking the Filter Bubble: A Semantic Pareto-DQN Framework for Multi-Objective Recommendation Cláudio Lúcio Do Val Lopes, Lucca Machado da Silva, André de Oliveira Brandão 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 Focus to learn more Submission history From: Claudio Lopes [view email] [v1] Tue, 23 Jun 2026 00:58:16 UTC (947 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)