An E-commerce recommendation system based on LightGBM Machine Learning Algorithm
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Date
2022-07
Journal Title
Journal ISSN
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Publisher
International Islamic University Chittagong
Abstract
In the dynamic realm of e-commerce, recommendation systems play a pivotal role in shaping user experiences and fueling business growth. This study advocates for a novel approach to online shopping recommendations, leveraging the power of the LightGBM machine learning algorithm. By focusing on item-to-item recommendations, our method ology seeks to elevate user satisfaction by swiftly and precisely offering customers highly personalized product choices. At the core of our recommendation system lies the fusion of item-to-item association analysis and user interactions, culminating in the delivery of accurate, real-time rec ommendations. This research contributes significantly to the e-commerce landscape by presenting a practical and scalable method that enriches customer experiences, conse quently amplifying sales and fostering customer loyalty. Through extensive testing and evaluation, our results underscore the transformative potential of the proposed item-to-item e-commerce recommendation system. This inno vative system stands poised to revolutionize digital commerce by providing users with pinpoint-accurate product recommendations. The seamless integration of machine learn ing, coupled with a focus on item-to-item relationships, not only expedites the decision making process for consumers but also cultivates a deeper connection between customers and the digital marketplace. In summary, our study demonstrates the capability of our approach to usher in a new era of precision and effectiveness in digital commerce, promis ing a paradigm shift in the way users discover and engage with products onlin
Description
This Dissertation is Submitted in Fulfillment
of the Requirements for the Degree of
Bachelor of Science (B.Sc.)
in
Computer Science and Engineering (CSE) Spring-2022
Keywords
E-commerce, recommendation system, LightGBM, machine learning, item to-item, personalizatio