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Dr. Mrudula M. Gudadhe is an Assistant Professor in the Department of Information Technology at Priyadarshini College of Engineering, Nagpur, India. Her academic background is associated with information technology education. Her contribution to this volume explores machine learning methods for e-commerce, consumer analytics, and recommendation systems.
The swift expansion of internet commerce has revolutionised the manner in which companies engage with customers, oversee product stocks, tailor shopping experiences, and formulate strategic choices. The growing accessibility of customer profiles, browsing histories, buy records, product evaluations, search enquiries, and clickstream information has generated prospects for utilising machine learning (ML) to comprehend consumer behaviour and enhance corporate efficacy. This chapter explores the essential principles, algorithms, frameworks, applications, and assessment techniques related to machine learning-based e-commerce and consumer analytics. It examines supervised learning, unsupervised learning, deep learning, natural language processing, collaborative filtering, content-based filtering, hybrid recommendation, and sequential recommendation. A theoretical structure is proposed for the amalgamation of data collection, preprocessing, consumer modelling, suggestion formulation, assessment, and ongoing feedback. Mathematical models for forecasting customer behaviour, rating recommendations, and assessing models are presented.