# Aspect-driven User Preference and News Representation Learning for News Recommendation

arXiv: https://arxiv.org/abs/2110.05792 · HTML: https://mlsanity.com/paper/2110.05792/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 65 references that are in the map.
- First posted on arXiv 12 Oct 2021.

## Builds on (most cited first, 12 of 12)

- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [DKN: Deep Knowledge-Aware Network for News Recommendation](https://mlsanity.com/paper/1801.08284/) (2018)
- [Sequential Recommender Systems: Challenges, Progress and Prospects](https://mlsanity.com/paper/2001.04830/) (2019)
- [A Survey on Session-based Recommender Systems](https://mlsanity.com/paper/1902.04864/) (2019)
- [NPA: Neural News Recommendation with Personalized Attention](https://mlsanity.com/paper/1907.05559/) (2019)
- [Neural News Recommendation with Attentive Multi-View Learning](https://mlsanity.com/paper/1907.05576/) (2019)
- [Graph Learning based Recommender Systems: A Review](https://mlsanity.com/paper/2105.06339/) (2021)
- [Graph Enhanced Representation Learning for News Recommendation](https://mlsanity.com/paper/2003.14292/) (2020)
- [Aspect-Aware Latent Factor Model: Rating Prediction with Ratings and Reviews](https://mlsanity.com/paper/1802.07938/) (2018)
- [Brain Intelligence: Go Beyond Artificial Intelligence](https://mlsanity.com/paper/1706.01040/) (2017)
- [Jointly Modeling Intra- and Inter-transaction Dependencies with Hierarchical Attentive Transaction Embeddings for Next-item Recommendation](https://mlsanity.com/paper/2006.04530/) (2020)
- [Hybrid Deep Embedding for Recommendations with Dynamic Aspect-Level Explanations](https://mlsanity.com/paper/2001.10341/) (2020)

## Built on by (most cited first, 3 of 3)

- [CPGRec+: A Balance-oriented Framework for Personalized Video Game Recommendations](https://mlsanity.com/paper/2604.14586/) (2026)
- [A Survey on LLM-based News Recommender Systems](https://mlsanity.com/paper/2502.09797/) (2025)
- [User recommendation system based on MIND dataset](https://mlsanity.com/paper/2209.06131/) (2022)

## Abstract (arXiv)

News recommender systems are essential for helping users to efficiently and effectively find out those interesting news from a large amount of news. Most of existing news recommender systems usually learn topic-level representations of users and news for recommendation, and neglect to learn more informative aspect-level features of users and news for more accurate recommendation. As a result, they achieve limited recommendation performance. Aiming at addressing this deficiency, we propose a novel Aspect-driven News Recommender System (ANRS) built on aspect-level user preference and news representation learning. Here, news aspect is fine-grained semantic information expressed by a set of related words, which indicates specific aspects described by the news. In ANRS, news aspect-level encoder and user aspect-level encoder are devised to learn the fine-grained aspect-level representations of user's preferences and news characteristics respectively, which are fed into click predictor to judge the probability of the user clicking the candidate news. Extensive experiments are done on the commonly used real-world dataset MIND, which demonstrate the superiority of our method compared with representative and state-of-the-art methods.