Personalized Recommendations in E-commerce: A Case Study on Sports and Outdoor Activities

Abstract

Nowadays, recommendation systems have become essential tools in e-commerce and social networks, offering personalized content, product, and service suggestions based on user behaviour and preferences. This document focuses on collaborative filtering, which makes recommendations using users' past product ratings. Several collaborative filtering algorithms will be compared, including Alternating Least Squares (ALS), Non-negative Matrix Factorization (NMF), Singular Value Decomposition (SVD), Bayesian Personalized Ranking (BPR), and Neural Collaborative Filtering (NCF). These methods will be tested on a dataset of sports and outdoor products from Amazon. The performance will be evaluated with two types of metrics: for rating predictions, RMSE, MAE, and R²; and for ranking, Precision, Recall, and nDCG.