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With the growth of e-commerce, product recommendations have become essential in providing customer satisfaction and developing sales. This paper sets forth a machine learning recommendation system that uses collaborative filtering, content-based filtering, and hybrid filtering to recommend products to customers with greater accuracy and relevance. The system improves the accuracy of the recommendations by basing them on user activity, product information, and purchase history. We discuss the implementation steps and results, system performance metrics, and potential for increased system adaptability and scalability.

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Product Recommendation System Using Machine Learning Techniques

  • 会议论文

  • 发表日期：

    2026年

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  • G. B. N. Jyothi,
  • Sai Charan Gurram,
  • Kalidindi Phani Lakshmi Maitreye,
  • Akula Rupa Bhavani,
  • Vinukonda Jahnavi,
  • Kolati Mounika

摘要

With the growth of e-commerce, product recommendations have become essential in providing customer satisfaction and developing sales. This paper sets forth a machine learning recommendation system that uses collaborative filtering, content-based filtering, and hybrid filtering to recommend products to customers with greater accuracy and relevance. The system improves the accuracy of the recommendations by basing them on user activity, product information, and purchase history. We discuss the implementation steps and results, system performance metrics, and potential for increased system adaptability and scalability.