Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2508.13978

Democratizing News Recommenders: Modeling Multiple Perspectives for News Candidate Generation with VQ-VAE

Hardy·Sebastian Padó·Amelie Wührl·Tanise Ceron
View original source ↗Read source document ↗Export metadata ↓

Abstract

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.

Explore related subjects

cs.IR

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Hardy, Sebastian Padó, Amelie Wührl, Tanise Ceron. 2026-05-12. Democratizing News Recommenders: Modeling Multiple Perspectives for News Candidate Generation with VQ-VAE. https://doi.org/10.1145/3805689.3812406

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

WebArxiv: A Reproducible Benchmark for Evaluating Multimodal Web Agents on arXiv Tasks

Foundation models now enable autonomous agents to interact with real-world websites, but existing benchmarks emphasize general-purpose browsing, underrepresent research-oriented environments and scholarly discovery workflows, and often depend on live sites whose changing content and structure undermine reproducibility. arXiv provides a realistic, reproducible, hierarchically structured, information-centric testbed without privacy-sensitive interactions. We introduce WebArxiv, a static-snapshot benchmark comprising 510 time-invariant tasks, each with a unique deterministic ground truth. Its diverse, realistic scholarly tasks go beyond simple information lookup and rule following to emphasize multi-constraint paper retrieval, fine-grained content extraction, and cross-paper comparison. Evaluations of a range of foundation-model-based web agents show that WebArxiv remains challenging. Behavioral analysis reveals that agents over-rely on fixed interaction histories, causing incomplete or repetitive reasoning. We therefore equip agents with a lightweight dynamic-memory mechanism for adaptive retrieval and reasoning over relevant context. The benchmark and code are available at https://anonymous.4open.science/r/74E4423BVNW/README.md.

cs.IR↗
arXiv2026

RQ-Reg: A Residual-Quantization-Based Framework for Continuous Value Prediction in Recommender Systems

Predicting continuous values such as watch-time and gross merchandise value (GMV) is a core problem in industrial recommendation systems. Its inherent difficulty stems from the highly complex and long-tailed distributions of the target signals, which are hard to model accurately. Existing regression methods typically rely on fixed parametric assumptions on the target distribution: overly simple assumptions underfit real-world data, whereas more intricate ones tend to sacrifice scalability and generalization. To address these limitations, we propose a sequence modeling framework based on residual quantization (RQ), in which the target continuous value is decomposed into a sequence of quantization codes that represent progressively finer approximations. The model autoregressively predicts these codes from coarse to fine granularity, with each step refining the residual error left by the previous one. To further improve the quality of the learned representations, we introduce an ordinal-aware representation learning objective that aligns the RQ code embedding space with the ordinal structure of target values, thereby yielding continuous representations of quantization codes and more accurate predictions. We conduct comprehensive experiments on public benchmarks for watch-time and lifetime value (LTV) prediction, together with a large-scale online A/B test for GMV prediction on an industrial short-video recommendation platform. Across all settings, the proposed method shows competitive performance among existing state-of-the-art approaches and generalizes well across diverse continuous value prediction scenarios.

cs.IR↗
arXiv2026

Cross-Country Code-Mixing for Generative Recommendation

Cross-country recommendation on modern e-commerce platforms is typically deployed with disjoint user and item ID spaces across markets, removing the shared anchors that conventional cross-domain methods rely on. Generative recommendation (GR) mitigates this by mapping items into a shared token space and training a unified model, but existing approaches keep behavior sequences strictly country-specific, so knowledge transfer occurs only at the parameter level and remains absent at the data level. Inspired by code-switching corpora in multilingual natural language processing, we propose CMRec, a cross-country GR framework that injects cross-country supervision at the data level via dual-constrained, context-aware code-mixing. CMRec first learns a shared semantic codebook from multi-modal content and behavioral co-occurrence across countries. It then uses this codebook to synthesize mixed-country sequences via token-level substitutions that satisfy both static (content) and dynamic (e.g., price, audience, popularity) constraints. Finally, it introduces a context-aware loss that reweights mixed samples according to their plausibility in the current sequence. Experiments on two real-world multi-country datasets and an online A/B test show that CMRec substantially improves recommendation quality in data-sparse countries while preserving performance in data-rich countries, achieving +1.77% advertising revenue and +2.64% orders on a large-scale e-commerce platform.

cs.IR↗