Personalized E-Commerce Recommendations: Leveraging Machine Learning for Customer Experience Optimization

Abstract

E-commerce ventures are increasingly turning to personalization as a key differentiator in the competitive digital market. Business in the marketplace is becoming more personal to gain closer engagements. Machine learning has transformed the possibility of personalizing shopping practices through the analysis of vast amounts of data that can discern user preferences and anticipate future actions. With such clever algorithms embedded in their websites, online retailers will be able to provide customers with a plethora of relevant, timely, and personalized product recommendations, leading to improved user satisfaction as well as business metrics, including click-through rates, conversion rates, and average order value. This study aimed to design, deploy, and evaluate machine learning algorithms that optimize product recommendations in a personalized e-commerce environment. The primary purpose is to develop scalable, efficient, and accurate recommendation systems that can be tailored to individual user preferences and adapt to real-time changes in behavior. The data from the given study were collected from a mid-sized e-commerce market in the United States over six months. It includes more than 150,000 interactions between users, over 25,000 individual users, and 10,000 products. The data is well-structured and contains several important dimensions that are vital for creating a personalized recommendation model. User demographics include age range with anonymity, gender, location (ZIP codes), and categories of customer loyalty. The history of browsing is captured through a session log that contains the browsed item, the amount of time spent on each page, the type of device, and the duration of the session. Exploratory Data Analysis (EDA) was essential for understanding the patterns, distributions, and relationships within the dataset, aiding in the assessment of features to select and in designing the model. In this research project, three machine learning algorithms were deployed, namely, Logistic Regression, Random Forest, and Support Vector Machines. To train and validate our models, we employed an 80:20 train-test split strategy, ensuring that 80% of user-product interactions were used for training. In comparison, 20% of the data were reserved for out-of-sample performance testing. The outcome clearly showed that the SVM model achieved the highest accuracy, making it the best-performing model among the other three. The introduction of machine learning-optimized recommendation systems to U.S. e-commerce systems will enable the personalization of services that were previously unachievable via rule-based, fixed solutions. The business strength of hyper-personalization has long been demonstrated by e-commerce giants such as Amazon and Target. In the works ahead, e-commerce recommendation systems are increasingly utilizing deep learning and contextual awareness to achieve a higher level of personalization.