Abstract
The impact of machine learning and artificial intelligence areas in e-commerce is growing. Algorithms from these areas help to grow sales and optimize various aspects of e-commerce operation, right from product selection to successful ordering of products. This work is focused on recommender system, navigation optimization, and product review summarization using machine learning and artificial intelligence techniques. Demographic content-based collaborative recommendation system framework is designed using hybrid similarity measure. Navigation optimization is done using the optimized prefix span algorithm. Gibbs sampling based latent Dirichlet allocation classifier framework is used to classify product reviews into positive, negative, and neutral, and represents it in bar chart form. These contributions will reduce human efforts while shopping using e-commerce site and helpful for high-quality user experience with more relative efficiency and satisfaction level.
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Patil, M., Rao, M. (2019). Studying the Contribution of Machine Learning and Artificial Intelligence in the Interface Design of E-commerce Site. In: Satapathy, S., Bhateja, V., Das, S. (eds) Smart Intelligent Computing and Applications . Smart Innovation, Systems and Technologies, vol 105. Springer, Singapore. https://doi.org/10.1007/978-981-13-1927-3_20
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DOI: https://doi.org/10.1007/978-981-13-1927-3_20
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