使用机器学习的电子商务产品推荐系统

Darshan M, A. C
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引用次数: 0

摘要

机器学习驱动的电子商务产品推荐系统的目标是提供一个完整的端到端网络平台,通过提供有见地的产品推荐来改善网上购物。该系统既有面向管理员的功能,也有面向用户的功能,安全访问需要登录凭证。为了从产品照片中提取信息,系统的后台使用了机器学习模型,特别是用于图像分析的卷积神经网络(CNN)。通过使用复杂的机器学习技术,用户的购买体验得到了提升,从而保证了相关推荐的准确性。总之,我们的研究强调了机器学习驱动的推荐系统对于提高消费者参与度和为电子商务平台创收的重要性。通过不断创新和改进,我们努力为企业提供最先进的资源,使其能够提供个性化和重要的购买体验。关键字用户体验、产品推荐、神经网络(CNN)
本文章由计算机程序翻译,如有差异,请以英文原文为准。
E-Commerce Product Recommendation System Using Machine Learning
The goal of the machine learning-powered e- commerce product recommendation system is to provide a complete, end-to-end web-based platform that improves online shopping by making insightful product recommendations. This system has features for administrators as well as users, and safe access requires login credentials. To extract information from product photos, the system's backend uses machine learning models, specifically convolutional neural networks (CNNs) for image analysis. The user's buying experience is enhanced by the use of sophisticated machine learning techniques, which guarantee relevant and accurate recommendations. To sum up, our study highlights how important machine learning-driven recommendation systems are for increasing consumer engagement and generating income for e-commerce platforms. Through constant innovation and improvement, we strive to provide businesses with state-of-the-art resources to enable them to provide individualized and significant purchasing experiences. Key Words: User Experience, Product Recommendation, Neural Network (CNN’s)
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