A Hybrid Recommender System Exploits Layered Convolutional Neural Networks.
Keywords:
E-commerce platforms, Customer preferences, Product recommendations, Deep learning techniques Latent preferences, Product images.Abstract
Enhancing user engagement and satisfaction in e-commerce platforms by incorporating customer preferences and interests into product recommendations is of paramount importance. However, accurately capturing these preferences, both explicit and implicit, from the vast array of products available in catalogues poses a significant challenge. In this study, we propose a novel approach that leverages deep learning techniques to extract latent preferences from product images. Our method focuses on extracting relevant data from the features of interest in product images, thereby enabling the identification of underlying customer preferences. We demonstrate the efficacy of our strategy by integrating this data extraction process into a product-based recommendation algorithm. Through experimental validation, we showcase the effectiveness of our approach in generating personalized suggestions tailored to individual customer preferences. Our findings underscore the potential of deep learning-based methodologies in harnessing visual cues to enhance the personalization of e-commerce recommendations, thereby improving user experience and engagement.
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