News Recommendation with Attention Mechanism
Tianrui Liu, Changxin Xu, Yuxin Qiao, Chufeng Jiang, Weisheng Chen
TL;DR
This paper introduces NRAM, an attention-based model for news recommendation, demonstrating its potential to enhance personalized news delivery on digital platforms.
Contribution
The paper presents NRAM, a novel attention mechanism-based approach for news recommendation, with an evaluation showing improved personalization capabilities.
Findings
NRAM significantly improves news personalization.
Attention mechanism enhances recommendation accuracy.
NRAM outperforms existing methods in tests.
Abstract
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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Taxonomy
TopicsAdvanced Text Analysis Techniques · Recommender Systems and Techniques · Sentiment Analysis and Opinion Mining