Abstract

Nowadays, recommendation systems are crucial in e-commerce. They deliver timely and relevant product suggestions to users. A well-crafted recommendation system can increase sales and create value for both buyers and sellers. In this research, we examine a large dataset containing sales data, product information, and customer contacts to gather statistical insights. We then introduce a collaborative filtering approach enhanced with data based on recency. Exploratory data analysis (EDA) techniques help identify relationships among variables, using key statistical tools and measures. To better understand consumer behavior, we generate grouped statistics such as purchasing trends by product category and customer age. The results of this research support the development of a collaborative filtering recommendation engine that incorporates recency weighting to improve product suggestions for online retail platforms.

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