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A Lightweight Content-Based News Recommendation System Using TF-IDF and Cosine Similarity
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Author(s): Snehal Rahul Rathi (Vishwakarma Institute of Technology, Pune, India), Aditi Meshram (Vishwakarma Institute of Information Technology, Pune, India), Ritik Narote (Vishwakarma Institute of Information Technology, Pune, India)and Shilpa Prashant Kalantri (Shah and Anchor Kutchhi Engineering College, Mumbai, India)
Copyright: 2026
Pages: 24
Source title:
Detecting Hate Speech in Human and AI-Generated Content: Techniques, Bias Mitigation, and Ethical Considerations
Source Author(s)/Editor(s): Mohammad Arsalan (Qatar University, Qatar), Mehul Mahrishi (Swami Keshvanand Institute of Technology, India), Ruchi Doshi (Universidad Azteca, Chalco, Mexico), Archika Jain (Swami Keshvanand Institute of Technology, India)and Chandrashekhar Goswami (Sir Padampat Singhania University, India)
DOI: 10.4018/979-8-3373-3063-1.ch003
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Abstract
The exponential growth in digital media has revolutionized news consumption. However, it has also led to significant challenges in filtering relevant content from the vast information pool. Personalized news recommendation systems are essential to ensure user engagement and satisfaction. This paper proposes a machine learning-based News Recommendation System leveraging Term Frequency-Inverse Document Frequency (TF-IDF) and cosine similarity to identify and suggest similar news articles based on content alone. The system is implemented using Python and Streamlit with additional features like category filtering, keyword search, and article export. Evaluation on a dataset of 1000+ real-world news article demonstrates the model's effectiveness and potential for scalable deployment.
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