# Is News Recommendation a Sequential Recommendation Task?

arXiv: https://arxiv.org/abs/2108.08984 · HTML: https://mlsanity.com/paper/2108.08984/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2021.

## Built on by (most cited first, 7 of 7)

- [Filter Bubbles in Recommender Systems: Fact or Fallacy -- A Systematic Review](https://mlsanity.com/paper/2307.01221/) (2023)
- [News Without Borders: Domain Adaptation of Multilingual Sentence Embeddings for Cross-lingual News Recommendation](https://mlsanity.com/paper/2406.12634/) (2024)
- [Peeling Back the Layers: An In-Depth Evaluation of Encoder Architectures in Neural News Recommenders](https://mlsanity.com/paper/2410.01470/) (2024)
- [User recommendation system based on MIND dataset](https://mlsanity.com/paper/2209.06131/) (2022)
- [A Look Into News Avoidance Through AWRS: An Avoidance-Aware Recommender System](https://mlsanity.com/paper/2407.09137/) (2024)
- [Token-Efficient Item Representation via Images for LLM Recommender Systems](https://mlsanity.com/paper/2503.06238/) (2025)
- [A Novel Mamba-based Sequential Recommendation Method](https://mlsanity.com/paper/2504.07398/) (2025)

## Abstract (arXiv)

News recommendation is often modeled as a sequential recommendation task, which assumes that there are rich short-term dependencies over historical clicked news. However, in news recommendation scenarios users usually have strong preferences on the temporal diversity of news information and may not tend to click similar news successively, which is very different from many sequential recommendation scenarios such as e-commerce recommendation. In this paper, we study whether news recommendation can be regarded as a standard sequential recommendation problem. Through extensive experiments on two real-world datasets, we find that modeling news recommendation as a sequential recommendation problem is suboptimal. To handle this challenge, we further propose a temporal diversity-aware news recommendation method that can promote candidate news that are diverse from recently clicked news, which can help predict future clicks more accurately. Experiments show that our approach can consistently improve various news recommendation methods.