# FUM: Fine-grained and Fast User Modeling for News Recommendation

arXiv: https://arxiv.org/abs/2204.04727 · HTML: https://mlsanity.com/paper/2204.04727/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2022.

## Built on by (most cited first, 4 of 4)

- [Prompt Learning for News Recommendation](https://mlsanity.com/paper/2304.05263/) (2023)
- [Personalized News Recommendation with Multi-granularity Candidate-aware User Modeling](https://mlsanity.com/paper/2504.14130/) (2025)
- [User recommendation system based on MIND dataset](https://mlsanity.com/paper/2209.06131/) (2022)
- [Multi-Granularity Vision Fastformer with Fusion Mechanism for Skin Lesion Segmentation](https://mlsanity.com/paper/2504.03108/) (2025)

## Abstract (arXiv)

User modeling is important for news recommendation. Existing methods usually first encode user's clicked news into news embeddings independently and then aggregate them into user embedding. However, the word-level interactions across different clicked news from the same user, which contain rich detailed clues to infer user interest, are ignored by these methods. In this paper, we propose a fine-grained and fast user modeling framework (FUM) to model user interest from fine-grained behavior interactions for news recommendation. The core idea of FUM is to concatenate the clicked news into a long document and transform user modeling into a document modeling task with both intra-news and inter-news word-level interactions. Since vanilla transformer cannot efficiently handle long document, we apply an efficient transformer named Fastformer to model fine-grained behavior interactions. Extensive experiments on two real-world datasets verify that FUM can effectively and efficiently model user interest for news recommendation.