New AI Framework Breaks Filter Bubbles in Recommender Systems

Recommender systems frequently suffer from "filter bubbles" and a lack of diversity because they typically prioritize immediate user engagement. This single-minded optimization, common in traditional Deep Q-Networks, struggles to balance user retention with crucial societal values such as information diversity and fairness for content providers. To address these limitations, a new multi-objective reinforcement learning framework has been introduced. This framework models recommendation as a semantic multi-objective Markov decision process. It integrates high-fidelity semantic embeddings with a Pareto-DQN agent, allowing it to treat engagement, diversity, and fairness as distinct, non-aggregable reward signals, thereby avoiding the pitfalls of combining them into a single score. Empirical tests on the MovieLens dataset demonstrated that this hypervolume-based action selection effectively disrupts the feedback loops that lead to semantic homogenization. The Pareto-DQN successfully maps the Pareto frontier, achieving significant improvements in auxiliary societal objectives like diversity and fairness with only minor impacts on user engagement. This work paves the way for developing more intrinsically aligned and responsible recommender systems.