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INTERNATIONAL JOURNAL OF CREATIVE RESEARCH THOUGHTS - IJCRT (IJCRT.ORG)
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Published Paper Details:
Review Paper: Advancing E-Commerce through Generalized Machine Learning and Deep Learning Techniques.
Authors
Nishant Sharma,Kirti Kushwah,Sandeep Soni,Pranshu Singh,Dhruv Singh
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Keywords
Deep Learning, Machine Learning, E-Commerce, Artificial Intelligence, Sentiment Analysis, Opinion Mining, Product Recommendation, Dynamic Pricing, Sales Prediction, Market Segmentation, Customer Segmentation, Prescriptive AI, Business Intelligence, Neural Networks, Support Vector Machines, Transformers, K-Means Clustering, Time-Series Forecasting, Time-Series Analysis, Linear Regression.
Abstract
This review paper explores the comprehensive
integration of advanced artificial intelligence (AI) techniques into
online marketplaces, with a primary focus on addressing challenges
encountered by businesses, particularly small enterprises. Motivated
by the imperative need for a tool that enables small businesses to
scale effectively through the incorporation of the latest
technological advancements, the project seeks to democratize access
to AI. Employing a multifaceted approach, it strategically tackles
the long-tail problem pervasive in online commerce. Techniques
such as Product Sentiment Analysis, Opinion Mining, Sales
Prediction, Dynamic Pricing, Product Recommendation, and
Customer Segmentation collectively contribute to mitigating
challenges associated with diverse product catalogs, enhancing
overall efficiency, and improving the user experience of online
marketplaces. Customer Segmentation employs K-means Clustering
to group customers based on similar characteristics, enabling
tailored marketing strategies, and enhancing personalization.
Dynamic pricing, addressed through Linear Regression, models
relationships influencing pricing dynamics, facilitating optimal
dynamic pricing based on relevant input variables. Opinion Mining
for Products leverages RoBERTa, a state-of-the-art natural language
processing model, to extract nuanced opinions and sentiments from
product reviews. Product Recommendation utilizes K-nearest
neighbors with Means to provide a personalized and user-centric
recommendation system, contributing to an enhanced customer
experience. Sales Prediction employs Time Series analysis to
analyze historical sales data, identify patterns, and make informed
predictions about future sales trends, ultimately improving decision-
making processes and optimizing inventory management. The
integration of these diverse AI modules into a cohesive backend
infrastructure ensures seamless communication and interoperability.
The creation of an admin panel for real-time statistics serves as a
centralized hub for stakeholders to visualize key metrics and make
data-driven decisions, facilitating efficient oversight and
management of AI-driven functionalities within the online
marketplace. In conclusion, this transformative project represents a
novel approach to empower small businesses through the strategic
integration of AI techniques, effectively addressing critical
challenges and leveraging advanced methodologies to revolutionize
online commerce and make AI-driven optimization accessible and
impactful for businesses navigating the complexities of the digital
landscape.
IJCRT's Publication Details
Unique Identification Number - IJCRT2402220
Paper ID - 251035
Author type - Indian Author
Page Number(s) - b870-b877
Pubished in - Volume 12 | Issue 2 | February 2024
DOI (Digital Object Identifier) -
No Of Downloads - 48
Author Country - India, 110032, Delhi, Delhi, 110032, Science and Technology
Publisher Name - IJPUBLICATION | www.ijcrt.org | ISSN : 2320-2882
E-ISSN Number - 2320-2882
Published Paper PDF : - http://www.ijcrt.org/papers/IJCRT2402220
Published Paper URL: : - http://ijcrt.org/viewfull.php?&p_id=IJCRT2402220
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Cite this article
Nishant Sharma,Kirti Kushwah,Sandeep Soni,Pranshu Singh,Dhruv Singh, "Review Paper: Advancing E-Commerce through Generalized Machine Learning and Deep Learning Techniques.", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.12, Issue 2, pp.b870-b877, February 2024, Available at :http://www.ijcrt.org/papers/IJCRT2402220.pdf
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The International Journal of Creative Research Thoughts (IJCRT) aims to explore advances in research pertaining to applied, theoretical and experimental Technological studies.
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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