Year: 2025
Type: article
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.
Author: Amol Nirphal
Institution: Savitribai Phule Pune University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4411123697