# HieRec: Hierarchical User Interest Modeling for Personalized News Recommendation

arXiv: https://arxiv.org/abs/2106.04408 · HTML: https://mlsanity.com/paper/2106.04408/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2021.

## Built on by (most cited first, 30 of 31)

- [Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback](https://mlsanity.com/paper/2303.05453/) (2023)
- [Prompt Learning for News Recommendation](https://mlsanity.com/paper/2304.05263/) (2023)
- [UniTRec: A Unified Text-to-Text Transformer and Joint Contrastive Learning Framework for Text-based Recommendation](https://mlsanity.com/paper/2305.15756/) (2023)
- [Going Beyond Local: Global Graph-Enhanced Personalized News Recommendations](https://mlsanity.com/paper/2307.06576/) (2023)
- [DIGAT: Modeling News Recommendation with Dual-Graph Interaction](https://mlsanity.com/paper/2210.05196/) (2022)
- [SPAR: Personalized Content-Based Recommendation via Long Engagement Attention](https://mlsanity.com/paper/2402.10555/) (2024)
- [Evaluation of LLMs-based Hidden States as Author Representations for Psychological Human-Centered NLP Tasks](https://mlsanity.com/paper/2503.00124/) (2025)
- [Don't Waste It: Guiding Generative Recommenders with Structured Human Priors via Multi-Head Decoding](https://mlsanity.com/paper/2511.10492/) (2025)
- [Modeling Multi-interest News Sequence for News Recommendation](https://mlsanity.com/paper/2207.07331/) (2022)
- [CROWN: A Novel Approach to Comprehending Users' Preferences for Accurate Personalized News Recommendation](https://mlsanity.com/paper/2310.09401/) (2023)
- [A Survey on LLM-based News Recommender Systems](https://mlsanity.com/paper/2502.09797/) (2025)
- [Co-NAML-LSTUR: A Combined Model with Attentive Multi-View Learning and Long- and Short-term User Representations for News Recommendation](https://mlsanity.com/paper/2507.20210/) (2025)
- [Quality-aware News Recommendation](https://mlsanity.com/paper/2202.13605/) (2022)
- [Understanding the Relation of User and News Representations in Content-Based Neural News Recommendation](https://mlsanity.com/paper/2207.14704/) (2022)
- [Time Matters: Enhancing Pre-trained News Recommendation Models with Robust User Dwell Time Injection](https://mlsanity.com/paper/2405.12486/) (2024)
- [Efficient Pointwise-Pairwise Learning-to-Rank for News Recommendation](https://mlsanity.com/paper/2409.17711/) (2024)
- [Personalized News Recommendation with Multi-granularity Candidate-aware User Modeling](https://mlsanity.com/paper/2504.14130/) (2025)
- [StreamProfileBench: A Benchmark for Fine-Grained User Profile Inference in Real-World Streaming Scenarios](https://mlsanity.com/paper/2605.25758/) (2026)
- [Item Cold Start Recommendation via Adversarial Variational Auto-encoder Warm-up](https://mlsanity.com/paper/2302.14395/) (2023)
- [Modeling Multiple User Interests using Hierarchical Knowledge for Conversational Recommender System](https://mlsanity.com/paper/2303.00311/) (2023)
- [GLoCIM: Global-view Long Chain Interest Modeling for news recommendation](https://mlsanity.com/paper/2408.00859/) (2024)
- [Privacy-Preserving Multimodal News Recommendation through Federated Learning](https://mlsanity.com/paper/2507.15460/) (2025)
- [E.A.R.T.H.: Structuring Creative Evolution through Model Error in Generative AI](https://mlsanity.com/paper/2507.18004/) (2025)
- [A hierarchy tree data structure for behavior-based user segment representation](https://mlsanity.com/paper/2508.01115/) (2025)
- [Improved Personalized Headline Generation via Denoising Fake Interests from Implicit Feedback](https://mlsanity.com/paper/2508.07178/) (2025)
- [Is This News Still Interesting to You?: Lifetime-aware Interest Matching for News Recommendation](https://mlsanity.com/paper/2508.13064/) (2025)
- [Modeling Stage-wise Evolution of User Interests for News Recommendation](https://mlsanity.com/paper/2603.10471/) (2026)
- [Hierarchical Multi-Persona Induction from User Behavioral Logs: Learning Evidence-Grounded and Truthful Personas](https://mlsanity.com/paper/2604.26120/) (2026)
- [Generative Long-term User Interest Modeling for Click-Through Rate Prediction](https://mlsanity.com/paper/2605.15905/) (2026)
- [MERIT: Mitigating Exposure Bias in Generative XMC for User-Interest Propensity Modeling](https://mlsanity.com/paper/2608.28931/) (2026)

## Abstract (arXiv)

User interest modeling is critical for personalized news recommendation. Existing news recommendation methods usually learn a single user embedding for each user from their previous behaviors to represent their overall interest. However, user interest is usually diverse and multi-grained, which is difficult to be accurately modeled by a single user embedding. In this paper, we propose a news recommendation method with hierarchical user interest modeling, named HieRec. Instead of a single user embedding, in our method each user is represented in a hierarchical interest tree to better capture their diverse and multi-grained interest in news. We use a three-level hierarchy to represent 1) overall user interest; 2) user interest in coarse-grained topics like sports; and 3) user interest in fine-grained topics like football. Moreover, we propose a hierarchical user interest matching framework to match candidate news with different levels of user interest for more accurate user interest targeting. Extensive experiments on two real-world datasets validate our method can effectively improve the performance of user modeling for personalized news recommendation.