Personalizing Product Recommendations using Collaborative Filtering in Online Retail: A Machine Learning Approach

In recent years, the adoption of machine learning algorithms in e-marketing has surged, particularly in the realm of personalized product recommendations tailored to customer preferences, which can significantly enhance customer engagement and boost sales. This paper addresses the challenge of identifying the most effective algorithm for crafting personalized recommendations in the context of online retail, leveraging a transactional data set. Specifically, the study centers on the utilization of the collaborative filtering algorithm, notably the widely acclaimed Alternate Least Squares algorithm, to scrutinize customers' purchase histories and discern patterns in their preferences. The objective is to construct a recommendation system capable of delivering tailored product suggestions to each customer based on their purchase history and the preferences of analogous customers. To assess the system's efficacy, performance metrics were employed, and a comparative analysis against other machine learning algorithms was conducted to evaluate the recommendation accuracy. The study highlights the potency of collaborative filtering in recognizing patterns in customer behavior and preferences, underscoring its potential to elevate customer engagement and drive sales in e-commerce.