Towards an effective recommendation algorithm for e-commerce

Abstract

E-commerce has revolutionized the way people shop, offering convenience and a vast array of products. However, with the sheer volume of items available, users often face difficulty in finding products that match their preferences. Recommendation algorithms play a crucial role in enhancing user experience by suggesting products that align with individual tastes and needs. Over the years, various recommendation systems have been developed, ranging from collaborative filtering to content-based and hybrid approaches. This project focuses on evaluating and comparing the effectiveness of four state-of-the-art recommendation algorithms: Matrix Factorization (MF), Bayesian Personalized Ranking (BPR), Neural Collaborative Filtering (NCF), and Neural Graph Collaborative Filtering (NGCF). Using the widely recognized MovieLens dataset, these algorithms are assessed through a comprehensive set of evaluation metrics, including Mean Average Precision (MAP), Normalized Discounted Cumulative Gain (NDCG), Precision, and Recall, to determine their performance in an e-commerce context. The project first compares the four algorithms to identify their relative strengths and weaknesses. Following this, a series of parameter tuning experiments will be conducted to examine the impact of various hyperparameters on performance. By systematically adjusting key parameters, the study aims to optimize each algorithm and assess how these modifications influence the evaluation metrics. Based on the results, the project recommends optimal parameter configurations to achieve the best performance, providing actionable insights for the implementation of effective recommendation systems in e-commerce platforms.