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.

Authors

Institutions

Publication Details

Journal
International Journal for Research in Applied Science and Engineering Technology
Published
2025-06-08
DOI
https://doi.org/10.22214/ijraset.2025.72197
Primary Topic
Technology and Data Analysis
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Product Recommendation System Using Deep Learning

Amol Nirphal
International Journal for Research in Applied Science and Engineering Technology
Technology and Data Analysis
article

Product Recommendation System Using Deep Learning

Amol Nirphal
article en

Abstract

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.

International Journal for Research in Applied Science and Engineering TechnologyVol. 13(6)
Savitribai Phule Pune University (IN)
Openalex Percentile: Top 82%
Technology and Data Analysis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.