An E-commerce recommendation system based on LightGBM Machine Learning Algorithm

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dc.contributor.authorHera, Mohammed Ashrafujjaman
dc.contributor.authorChowdhury, Md.Injamamul Hoque
dc.contributor.authorTohidu, Mohammad
dc.date.accessioned2024-03-30T06:02:29Z
dc.date.available2024-03-30T06:02:29Z
dc.date.issued2022-07
dc.descriptionThis Dissertation is Submitted in Fulfillment of the Requirements for the Degree of Bachelor of Science (B.Sc.) in Computer Science and Engineering (CSE) Spring-2022en_US
dc.description.abstractIn 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 onlinen_US
dc.identifier.urihttp://dspace.iiuc.ac.bd:8080/xmlui/handle/123456789/8143
dc.language.isoenen_US
dc.publisherInternational Islamic University Chittagongen_US
dc.subjectE-commerceen_US
dc.subjectrecommendation systemen_US
dc.subjectLightGBMen_US
dc.subjectmachine learningen_US
dc.subjectitem to-itemen_US
dc.subjectpersonalizatioen_US
dc.titleAn E-commerce recommendation system based on LightGBM Machine Learning Algorithmen_US
dc.typeThesisen_US

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Bachelor of Computer Science and Engineering (CSE)