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NPA: Neural News Recommendation with Personalized Attention

arXiv 1907.05559 ↗First posted 12 Jul 2019cs.IR cs.CLcited by 73

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Abstract (arXiv)

News recommendation is very important to help users find interested news and alleviate information overload. Different users usually have different interests and the same user may have various interests. Thus, different users may click the same news article with attention on different aspects. In this paper, we propose a neural news recommendation model with personalized attention (NPA). The core of our approach is a news representation model and a user representation model. In the news representation model we use a CNN network to learn hidden representations of news articles based on their titles. In the user representation model we learn the representations of users based on the representations of their clicked news articles. Since different words and different news articles may have different informativeness for representing news and users, we propose to apply both word- and news-level attention mechanism to help our model attend to important words and news articles. In addition, the same news article and the same word may have different informativeness for different users. Thus, we propose a personalized attention network which exploits the embedding of user ID to generate the query vector for the word- and news-level attentions. Extensive experiments are conducted on a real-world news recommendation dataset collected from MSN news, and the results validate the effectiveness of our approach on news recommendation.

Key facts. Cited by 73 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Jul 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 18; 2022: 12; 2023: 18; 2024: 12; 2025: 11; 2026: 2.

73 papers.

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  1. 2021FedKD: Communication Efficient Federated Learning via Knowledge Distillationcited by 187
  2. 2023When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunitiescited by 90
  3. 2021SimpleX: A Simple and Strong Baseline for Collaborative Filteringcited by 65
  4. 2021A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendationcited by 59
  5. 2023ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Modelscited by 54
  6. 2023LLMRec: Benchmarking Large Language Models on Recommendation Taskcited by 30
  7. 2023Prompt Learning for News Recommendationcited by 25
  8. 2024MMREC: LLM Based Multi-Modal Recommender Systemcited by 22
  9. 2021Rethinking InfoNCE: How Many Negative Samples Do You Need?cited by 19
  10. 2021Training Large-Scale News Recommenders with Pretrained Language Models in the Loopcited by 18
  11. 2021Social Recommendation with Self-Supervised Metagraph Informax Networkcited by 18
  12. 2021NewsBERT: Distilling Pre-trained Language Model for Intelligent News Applicationcited by 17
38 more
  1. 2023Recent Developments in Recommender Systems: A Surveycited by 14
  2. 2023LKPNR: LLM and KG for Personalized News Recommendation Frameworkcited by 13
  3. 2024EB-NeRD: A Large-Scale Dataset for News Recommendationcited by 12
  4. 2021Why Do We Click: Visual Impression-aware News Recommendationcited by 10
  5. 2023Going Beyond Local: Global Graph-Enhanced Personalized News Recommendationscited by 10
  6. 2021USER: A Unified Information Search and Recommendation Model based on Integrated Behavior Sequencecited by 9
  7. 2021From Intrinsic to Counterfactual: On the Explainability of Contextualized Recommender Systemscited by 9
  8. 2022Positive, Negative and Neutral: Modeling Implicit Feedback in Session-based News Recommendationcited by 9
  9. 2023SmartBook: AI-Assisted Situation Report Generation for Intelligence Analystscited by 9
  10. 2023KHAN: Knowledge-Aware Hierarchical Attention Networks for Accurate Political Stance Predictioncited by 8
  11. 2021Neural News Recommendation with Collaborative News Encoding and Structural User Encodingcited by 7
  12. 2022RADio -- Rank-Aware Divergence Metrics to Measure Normative Diversity in News Recommendationscited by 7
  13. 2022DIGAT: Modeling News Recommendation with Dual-Graph Interactioncited by 7
  14. 2021NewsEmbed: Modeling News through Pre-trained Document Representationscited by 6
  15. 2022FedCL: Federated Contrastive Learning for Privacy-Preserving Recommendationcited by 6
  16. 2024SPAR: Personalized Content-Based Recommendation via Long Engagement Attentioncited by 6
  17. 2025DLRREC: Denoising Latent Representations via Multi-Modal Knowledge Fusion in Deep Recommender Systemscited by 5
  18. 2023Disentangled Representation for Diversified Recommendationscited by 4
  19. 2023Modeling User Viewing Flow Using Large Language Models for Article Recommendationcited by 4
  20. 2024News Recommendation with Category Description by a Large Language Modelcited by 4
  21. 2025Rethinking Byzantine Robustness in Federated Recommendation from Sparse Aggregation Perspectivecited by 4
  22. 2021Global Context Enhanced Social Recommendation with Hierarchical Graph Neural Networkscited by 3
  23. 2021Aspect-driven User Preference and News Representation Learning for News Recommendationcited by 3
  24. 2022Few-shot News Recommendation via Cross-lingual Transfercited by 3
  25. 2022VLSNR:Vision-Linguistics Coordination Time Sequence-aware News Recommendationcited by 3
  26. 2023PerPLM: Personalized Fine-tuning of Pretrained Language Models via Writer-specific Intermediate Learning and Promptscited by 3
  27. 2024JobFormer: Skill-Aware Job Recommendation with Semantic-Enhanced Transformercited by 3
  28. 2021From Implicit to Explicit feedback: A deep neural network for modeling sequential behaviours and long-short term preferences of online userscited by 2
  29. 2022Targeted Data Poisoning Attack on News Recommendation System by Content Perturbationcited by 2
  30. 2022Denoising Neural Network for News Recommendation with Positive and Negative Implicit Feedbackcited by 2
  31. 2022Modeling Multi-interest News Sequence for News Recommendationcited by 2
  32. 2023Dimension Independent Mixup for Hard Negative Sample in Collaborative Filteringcited by 2
  33. 2023CROWN: A Novel Approach to Comprehending Users' Preferences for Accurate Personalized News Recommendationcited by 2
  34. 2025Fact-Preserved Personalized News Headline Generationcited by 2
  35. 2025A Survey on LLM-based News Recommender Systemscited by 2
  36. 2025Co-NAML-LSTUR: A Combined Model with Attentive Multi-View Learning and Long- and Short-term User Representations for News Recommendationcited by 2
  37. 2021Learning to Select Historical News Articles for Interaction based Neural News Recommendationcited by 1
  38. 2021Improving Next-Application Prediction with Deep Personalized-Attention Neural Networkcited by 1

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