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

The article presents a novel multi-objective reinforcement learning framework called Semantic Pareto-DQN for improving recommender systems. By formalizing the recommendation process as a semantic multi-objective Markov decision process, it employs a Pareto-DQN agent that optimizes for engagement, diversity, and fairness without aggregating these objectives into a single reward signal. Empirical results from the MovieLens dataset demonstrate that this approach enhances societal objectives while maintaining user engagement, offering a significant advancement for practitioners aiming to build responsible AI systems that mitigate filter bubbles.