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@article{171267,
        author = {Rohit Vishwakarma and Dr. Aakanksha Chopra},
        title = {Recommendation System for E-Commerce Platforms to Suggest Products Based on Browsing History},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {7},
        pages = {3898-3901},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171267},
        abstract = {On e-commerce platforms, recommendation algorithms play a critical role in improving user experience and increasing sales by customizing product recommendations. The use of machine learning (ML) techniques to create a recommendation system that uses a user's browsing history to make pertinent product recommendations is examined in this study. With an emphasis on content-based filtering, collaborative filtering, and hybrid models, this study shows how combining these strategies enhances suggestion relevance and accuracy. Important experimental findings demonstrate the system's capacity to provide highly tailored product recommendations, indicating its potential to turn e-commerce platforms into sophisticated shopping assistants.},
        keywords = {E-commerce, recommendation systems, machine learning, content-based and collaborative filtering, hybrid models, personalization, browsing history, and user behaviour.},
        month = {January},
        }