Co-NAML-LSTUR: A Combined Model with Attentive Multi-View Learning and Long- and Short-term User Representations for News Recommendation

TL;DR

This paper introduces Co-NAML-LSTUR, a hybrid news recommendation model that combines multi-view news encoding with hierarchical user modeling, effectively capturing user interests at multiple scales to improve recommendation accuracy especially with limited data.

Contribution

The paper proposes a novel hybrid framework integrating NAML and LSTUR, utilizing BERT embeddings for enhanced semantic understanding and efficient dual-scale user interest modeling.

Findings

Significant performance improvements over baselines in AUC and MRR.

Effective modeling of multi-view news features and user interests.

Demonstrated efficiency and practicality for resource-limited settings.

Abstract

News recommendation systems play a critical role in alleviating information overload by delivering personalized content. A key challenge lies in jointly modeling multi-view representations of news articles and capturing the dynamic, dual-scale nature of user interests-encompassing both short- and long-term preferences. Prior methods often rely on single-view features or insufficiently model user behavior across time. In this work, we introduce Co-NAML-LSTUR, a hybrid news recommendation framework that integrates NAML for attentive multi-view news encoding and LSTUR for hierarchical user modeling, designed for training on limited data resources. Our approach leverages BERT-based embeddings to enhance semantic representation. We evaluate Co-NAML-LSTUR on two widely used benchmarks, MIND-small and MIND-large. Results show that our model significantly outperforms strong baselines, achieving improvements over NRMS by 1.55% in AUC and 1.15% in MRR, and over NAML by 2.45% in AUC and 1.71% in MRR. These findings highlight the effectiveness of our efficiency-focused hybrid model, which combines multi-view news modeling with dual-scale user representations for practical, resource-limited resources rather than a claim to absolute state-of-the-art (SOTA). The implementation of our model is publicly available at https://github.com/MinhNguyenDS/Co-NAML-LSTUR