mlsanityData to 7 Oct 2026

News Recommendation with Attention Mechanism

arXiv 2402.07422 ↗First posted 12 Feb 2024cs.AIcited by 14

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Abstract (arXiv)

This paper explores the area of news recommendation, a key component of online information sharing. Initially, we provide a clear introduction to news recommendation, defining the core problem and summarizing current methods and notable recent algorithms. We then present our work on implementing the NRAM (News Recommendation with Attention Mechanism), an attention-based approach for news recommendation, and assess its effectiveness. Our evaluation shows that NRAM has the potential to significantly improve how news content is personalized for users on digital news platforms.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 10 of its 31 references that are in the map. First posted on arXiv 12 Feb 2024.

Builds on

10 of 31 references.

References · 10

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  4. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
  5. 2016Wide & Deep Learning for Recommender Systemscited by 745
  6. 2018DKN: Deep Knowledge-Aware Network for News Recommendationcited by 137
  7. 2021Personalized News Recommendation: Methods and Challengescited by 30
  8. 2024DeepGI: An Automated Approach for Gastrointestinal Tract Segmentation in MRI Scanscited by 13
  9. 2022Joint Spatio-Temporal Precoding for Practical Non-Stationary Wireless Channelscited by 6
  10. 2022Proofs and Supplementary Material: Unified Characterization and Precoding for Non-Stationary Channelscited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 13; 2025: 0; 2026: 1.

14 papers.

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