International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 07 Issue: 05 | May 2020 www.irjet.net p-ISSN: 2395-0072 © 2020, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 4511 A Review on the Role of Machine Learning in Enhancing User Experience in E-commerce Applications Tanvi Chadaga 1 , Mahantesh Shivagunde 2 , Jyothi Shetty 3 , Sharvani G S 4 1 Dept. Of Computer Science and Engineering, RV College of Engineering, Bengaluru, Karnataka, India, 2 Dept. Of Computer Science and Engineering, RV College of Engineering, Bengaluru, Karnataka, India, 3 Assistant Professor, Dept. Of Computer Science and Engineering, RV College of Engineering, Bengaluru, Karnataka, India 4 Associate Professor, Dept. Of Computer Science and Engineering, RV College of Engineering, Bengaluru, Karnataka, India ---------------------------------------------------------------------***---------------------------------------------------------------------- Abstract - Machine Learning is now becoming one of the leading topics in the technology world. So it comes to no one’s surprise that leading e-commerce companies would look into machine learning to incorporate into their commerce application. User Experience is an important aspect for smooth business operation since customer satisfaction is the key to greater productivity. To automise several operations within the ecommerce application has proven advantageous and profitable for most business operators. This paper explores and reviews the various applications of machine learning in e- commerce as a tool to enhance the user experience. Key Words: Machine Learning, User Experience, E- commerce 1. INTRODUCTION E-commerce stands for Electronic Commerce. It implies buying and selling goods and services through Electronic media and the Internet. A more complete definition of E- commerce is given in [1] : E-commerce is the use of electronic communications and digital information processing technology in business transactions to create, transform, and redefine relationships for value creation between or among organizations, and between organizations and individuals. E-commerce has transformed the way in which organizations interact with their consumers and partners. With rapid development in Information and Communication Technology (ICT), there has been an unprecedented growth in the E-commerce industry all over the world. According to studies, global E-commerce sales are expected to reach $4.2 trillion in 2020 and $6.5 trillion by 2023. With increased number of transactions happening on E-commerce platforms, organizations have a large corpus of data about these transactions. This data is being leveraged to improve the customer satisfaction and User Experience. Artificial Intelligence (AI), Big Data Analytics (BDA), Data Mining and Machine Learning (ML) are the tools being used to extract knowledge from the data which can be used to improve the User Experience by means of recommendations, dynamic pricing, optimized search results etc. 1.1 Machine Learning Propelled by an increase in computational power, memory, storage and the generation of huge amounts of data, computers are being employed to perform a wide- range of complex-tasks with commendable accuracy. Machine Learning comprises elements of mathematics, statistics and computer science. It is being leveraged in both industry and academia to develop intelligent products with the ability to make accurate predictions using diverse sources of data[2]. The key beneficiaries of the Machine Learning and Artificial Intelligence technologies have been the industries which are able to collect large amounts of data from their business. The large corpus of data being collected in E-commerce industries offer tremendous potential to enhance business opportunities and to improve customer experience. Machine learning techniques employ algorithms - a set of mathematical procedures which describe the relationships between variables. The primary concern in any Machine Learning application is an accurate prediction. Given a set of input features representing the input data, Machine Learning algorithms try to predict some kind of unknown characteristic about the input features or find some pattern from the input data. The majority of Machine Learning algorithms can be categorized into two types of learning techniques: Supervised learning and Unsupervised learning. Supervised learning refers to a set of Machine Learning algorithms in which a model is trained on a range of input features which are associated with a known outcome. In E-commerce this might represent training a model to relate a customer’s characteristics (e.g., age, location, purchase history) to certain outcome (product recommendation). There are two main types of supervised learning algorithms: Classification and Regression. Regression is a type of supervised learning which predicts an output value based on the input features