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News Recommendation with Attention Mechanism

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arxiv 2402.07422 v2 pith:24EGMA4D submitted 2024-02-12 cs.AI

classification cs.AI
keywords newsrecommendationattentionmechanismnramalgorithmsapproacharea
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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.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Real-World fNIRS-Based Brain-Computer Interfaces: Benchmarking Deep Learning and Classical Models in Interactive Gaming

    q-bio.NC 2025-05 reject novelty 4.0 of 10

    The paper reports high rest/task classification accuracy for fNIRS during a tennis game, but asymmetric augmentation of the rest class confounds the benchmark.

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