# D-HAN: Dynamic News Recommendation with Hierarchical Attention Network

arXiv: https://arxiv.org/abs/2112.10085 · HTML: https://mlsanity.com/paper/2112.10085/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 50 references that are in the map.
- First posted on arXiv 19 Dec 2021.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [Session-based Recommendations with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06939/) (2015)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding](https://mlsanity.com/paper/1809.07426/) (2018)
- [A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions](https://mlsanity.com/paper/2109.12843/) (2021)
- [DKN: Deep Knowledge-Aware Network for News Recommendation](https://mlsanity.com/paper/1801.08284/) (2018)
- [Neural News Recommendation with Attentive Multi-View Learning](https://mlsanity.com/paper/1907.05576/) (2019)
- [POLYGLOT-NER: Massive Multilingual Named Entity Recognition](https://mlsanity.com/paper/1410.3791/) (2014)
- [Personalized News Recommendation: Methods and Challenges](https://mlsanity.com/paper/2106.08934/) (2021)
- [Session-aware Recommendation: A Surprising Quest for the State-of-the-art](https://mlsanity.com/paper/2011.03424/) (2020)
- [Personalized News Recommendation with Context Trees](https://mlsanity.com/paper/1303.0665/) (2013)
- [Extreme Classification via Adversarial Softmax Approximation](https://mlsanity.com/paper/2002.06298/) (2020)

## Built on by (most cited first, 1 of 1)

- [A Survey on LLM-based News Recommender Systems](https://mlsanity.com/paper/2502.09797/) (2025)

## Abstract (arXiv)

News recommendation models often fall short in capturing users' preferences due to their static approach to user-news interactions. To address this limitation, we present a novel dynamic news recommender model that seamlessly integrates continuous time information to a hierarchical attention network that effectively represents news information at the sentence, element, and sequence levels. Moreover, we introduce a dynamic negative sampling method to optimize users' implicit feedback. To validate our model's effectiveness, we conduct extensive experiments on three real-world datasets. The results demonstrate the effectiveness of our proposed approach.