# Neural News Recommendation with Attentive Multi-View Learning

arXiv: https://arxiv.org/abs/1907.05576 · HTML: https://mlsanity.com/paper/1907.05576/

## Key facts

- Cited by 59 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2019.

## Built on by (most cited first, 30 of 59)

- [FedKD: Communication Efficient Federated Learning via Knowledge Distillation](https://mlsanity.com/paper/2108.13323/) (2021)
- [A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation](https://mlsanity.com/paper/2104.13030/) (2021)
- [ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Models](https://mlsanity.com/paper/2305.06566/) (2023)
- [Prompt Learning for News Recommendation](https://mlsanity.com/paper/2304.05263/) (2023)
- [FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning](https://mlsanity.com/paper/2206.03200/) (2022)
- [Rethinking InfoNCE: How Many Negative Samples Do You Need?](https://mlsanity.com/paper/2105.13003/) (2021)
- [Training Large-Scale News Recommenders with Pretrained Language Models in the Loop](https://mlsanity.com/paper/2102.09268/) (2021)
- [NewsBERT: Distilling Pre-trained Language Model for Intelligent News Application](https://mlsanity.com/paper/2102.04887/) (2021)
- [LKPNR: LLM and KG for Personalized News Recommendation Framework](https://mlsanity.com/paper/2308.12028/) (2023)
- [Scalable Neural Contextual Bandit for Recommender Systems](https://mlsanity.com/paper/2306.14834/) (2023)
- [Why Do We Click: Visual Impression-aware News Recommendation](https://mlsanity.com/paper/2109.12651/) (2021)
- [Going Beyond Local: Global Graph-Enhanced Personalized News Recommendations](https://mlsanity.com/paper/2307.06576/) (2023)
- [USER: A Unified Information Search and Recommendation Model based on Integrated Behavior Sequence](https://mlsanity.com/paper/2109.15012/) (2021)
- [Neural News Recommendation with Collaborative News Encoding and Structural User Encoding](https://mlsanity.com/paper/2109.00750/) (2021)
- [RADio -- Rank-Aware Divergence Metrics to Measure Normative Diversity in News Recommendations](https://mlsanity.com/paper/2209.13520/) (2022)
- [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)
- [Semi-FairVAE: Semi-supervised Fair Representation Learning with Adversarial Variational Autoencoder](https://mlsanity.com/paper/2204.00536/) (2022)
- [Modeling User Viewing Flow Using Large Language Models for Article Recommendation](https://mlsanity.com/paper/2311.07619/) (2023)
- [EmbSum: Leveraging the Summarization Capabilities of Large Language Models for Content-Based Recommendations](https://mlsanity.com/paper/2405.11441/) (2024)
- [News Recommendation with Category Description by a Large Language Model](https://mlsanity.com/paper/2405.13007/) (2024)
- [News Without Borders: Domain Adaptation of Multilingual Sentence Embeddings for Cross-lingual News Recommendation](https://mlsanity.com/paper/2406.12634/) (2024)
- [Diffusion-based Multi-modal Synergy Interest Network for Click-through Rate Prediction](https://mlsanity.com/paper/2508.21460/) (2025)
- [Deep Dynamic Neural Network to trade-off between Accuracy and Diversity in a News Recommender System](https://mlsanity.com/paper/2103.08458/) (2021)
- [Aspect-driven User Preference and News Representation Learning for News Recommendation](https://mlsanity.com/paper/2110.05792/) (2021)
- [Few-shot News Recommendation via Cross-lingual Transfer](https://mlsanity.com/paper/2207.14370/) (2022)
- [VLSNR:Vision-Linguistics Coordination Time Sequence-aware News Recommendation](https://mlsanity.com/paper/2210.02946/) (2022)
- [JobFormer: Skill-Aware Job Recommendation with Semantic-Enhanced Transformer](https://mlsanity.com/paper/2404.04313/) (2024)
- [PerSEval: Assessing Personalization in Text Summarizers](https://mlsanity.com/paper/2407.00453/) (2024)
- [From Implicit to Explicit feedback: A deep neural network for modeling sequential behaviours and long-short term preferences of online users](https://mlsanity.com/paper/2107.12325/) (2021)

## Abstract (arXiv)

Personalized news recommendation is very important for online news platforms to help users find interested news and improve user experience. News and user representation learning is critical for news recommendation. Existing news recommendation methods usually learn these representations based on single news information, e.g., title, which may be insufficient. In this paper we propose a neural news recommendation approach which can learn informative representations of users and news by exploiting different kinds of news information. The core of our approach is a news encoder and a user encoder. In the news encoder we propose an attentive multi-view learning model to learn unified news representations from titles, bodies and topic categories by regarding them as different views of news. In addition, we apply both word-level and view-level attention mechanism to news encoder to select important words and views for learning informative news representations. In the user encoder we learn the representations of users based on their browsed news and apply attention mechanism to select informative news for user representation learning. Extensive experiments on a real-world dataset show our approach can effectively improve the performance of news recommendation.