A Deep Learning Approach for Product Recommendation using ResNet-50 CNN Model

Product recommendation systems work well at boosting user engagement, increasing sales, and enhancing customer satisfaction by making personalized and relevant suggestions for goods based on users’ preferences and behaviors. However, these systems face several challenges, including data sparsity, scalability, and bias. Recent studies have looked into the creation of image-based recommendation systems using deep learning and transfer learning methods, such as Reverse Image Search, to overcome these difficulties. This study discusses the use of deep learning and transfer learning techniques, including Reverse Image Search, to create a product recommendation system based on images. The system draws features from images and produces five product recommendations based on similarities to other images using an already trained CNN model called ResNet-50. The study highlights the effectiveness of the approach while acknowledging potential limitations such as bias and scalability. To overcome these limitations, the study proposes further improvements, such as incorporating user preferences into the recommendation system and using more advanced deep learning models. The study concludes that the ResNet-50 model is a promising approach for image-based product recommendations, but further improvements are necessary to enhance its accuracy and scalability.