Recommendation System for E-Commerce Using Collaborative Filtering

Abstract

In the swiftly evolving domain of online commerce, the significance of recommendation systems has risen alongside the rapid expansion of internet usage over the past decade. As online commerce continues to thrive, recommendation systems serve a crucial function in steering users towards pertinent products amidst the expansive online environment. Nonetheless, conventional collaborative filtering algorithms often encounter challenges such as sparse data and shifting user preferences, necessitating innovative approaches. Our proposed recommendation system aims to tackle these hurdles by seamlessly blending collaborative filtering and content-based filtering methodologies. It will offer product suggestions for both new and existing users. Through thorough examination of users' past purchasing behaviors, patterns, and feedback, our system customizes recommendations precisely to cater to existing users' needs. The initial stage involves feature extraction, wherein both content-based and collaborative features are obtained by creating user profiles, computing content similarity, identifying related items, generating recommendations, and suggesting items. Additionally, new users will receive recommendations for novel and trending products. Leveraging the Walmart Product rating Dataset, our system continuously enhances recommendations based on evolving user interactions, thus optimizing engagement and satisfaction levels. This study underscores the pivotal role of advanced recommendation techniques in transforming the online commerce landscape, ensuring informed purchasing decisions, heightened user satisfaction, and increased sales.