Democratizing News Recommenders: Modeling Multiple Perspectives for News Candidate Generation with VQ-VAE
For designers of news recommender systems, this work provides a novel mechanism to explicitly control democratic values in recommendations, addressing a gap in existing systems that lack such configurability.
The paper tackles the lack of explicit value control in news recommender systems by introducing A2CG, a candidate generation method that uses VQ-VAE and aspect-based diversity injection. It enables continuous calibration between personalization and democratic alignment without retraining, producing diverse and serendipitous candidates.
News Recommender Systems (NRS) shape what users read, whose perspectives they encounter, and influence public discourse. Yet their design is value-laden: intentionally or not, NRS can embed undesired values in recommendation procedures, such as excluding underrepresented voices or favoring certain viewpoints, which may conflict with democratic goals. Existing solutions also lack mechanisms to explicitly control these values. Therefore, we introduce an approach that parameterizes NRS to support different democratic goals. We propose Aspect-Aware Candidate Generation (A2CG), a normatively configurable procedure for the candidate generation stage of NRS that allows designers to shape diversity in recommendations. Unlike prior work that only re-ranks candidates, A2CG introduces diversity at the start of the recommendation pipeline. A2CG represents articles along multiple diversity aspects: sentiment, political leaning, topic, and media framing. User interests are encoded using a Vector Quantized VAE, while a decoder-only model predicts the next article aspects users are likely to engage with. To broaden exposure to perspectives, A2CG injects diversity during retrieval by selectively flipping aspects in the predicted query, allowing candidate diversity to be tuned toward specific democratic models. Our method enables normative configurations that existing NRS cannot express. Unlike baselines with fixed structural biases, A2CG supports continuous calibration between democratic ideals without retraining. Empirically, A2CG generates novel, diverse, and serendipitous candidates while providing explicit parameter-driven control over the trade-off between personalization and democratic alignment. Rather than aiming for pointwise superiority, A2CG's main contribution lies in its controllability and ability to express flexible normative configurations.