Product Recommendation System Using Deep Learning

Abstract

This term paper investigates the improvement of a product recommendation framework for basic supply items, leveraging the power of profound learning and the back vector machine (SVM) algorithm. The framework points to supply personalized recommendations to clients, improving their shopping experience and possibly expanding deals for retailers. The proposed demonstrate joins a profound learning design to capture complicated designs and connections between clients and products, taken after by an SVM classifier to refine the recommendations. The framework is assessed utilizing a comprehensive dataset of client buy history and product data. The comes about illustrate the effectiveness of the proposed approach in producing precise and relevant product proposals, outflanking traditional recommendation strategies.

Introduction

In today’s e-commerce-driven world, personalized product recommendation systems are vital across industries, especially in the complex food sector with its diverse products and customer preferences. Traditional methods struggle to capture intricate user behaviors and product relations, so this research proposes a hybrid system combining deep learning with Support Vector Machine (SVM) classification. This integration enables highly accurate and personalized grocery product recommendations, enhancing customer experience and boosting retailer sales.

The paper reviews related research including lean manufacturing for product safety, a CNN-based deep learning recommendation system for textiles, and a novel framework (FEATURE) for recommending unique product features based on competitor and social media data.

The proposed system’s architecture features a modular backend for real-time user behavior tracking and machine learning analysis, linked with a user-friendly frontend involving registration, face capture, and feature extraction. The SVM classifier optimally separates product classes to improve prediction accuracy.

Key software tools include Python for programming, Anaconda Navigator for package management, Spyder as the development environment, and DBBrowser for SQLite to manage databases.

Conclusion

This study examines the development of a new food recommendation system. By integrating deep learning into the Support Vector Machine (SVM) algorithm, the system aims to provide customers with highly personalized and accurate product suggestions. This approach uses the power of deep learning to reveal complex patterns in customer behavior and product relationships, but the SVM classifier improves these predictions for optimal accuracy. The resulting system is expected to improve the customer\'s purchasing experience, increase retailer sales, and provide valuable insight into consumer preferences.

References

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Copyright

Copyright © 2025 Amol Nirphal, Shubham Sabale, Swapnil Pawar, Prof. Varsha Kulkarni. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.