Machine Learning Driven Product Recommendations for Enhanced Shopping Cart Personalization
Gauri Kitukale, Nitin Arvind Shelke, Rohit Agrawal, Navneet Pratap Singh, Siddharth Quamara, Singara Singh Kasana · 2024
Recommendation systems are a crucial part of machine learning algorithms that provide users with pertinent recommendations based on their requests. There are numerous implicit and explicit features that can estimate user preferences, necessitating the need for accurate and scalable algorithms and a highly available and scalable system. Consumers find it more challenging to find information pertinent to their interests as the number of information-accessible sources increases along with the demand for e-commerce websites. In order to provide users with individualized content and services, recommender systems comb through enormous amounts of dynamically generated data. This paper presents the implementation of a recommendation system by employing various machine-learning techniques. The primary objective of this implementation is to showcase a specific solution and facilitate the comparison of different models. The proposed system is tested and evaluated on standard benchmark datasets, showing significant results.