A Brief Survey of Machine Learning and Deep Learning Techniques for E-Commerce Research

Abstract

The rapid growth of e-commerce has significantly increased the demand for advanced techniques to address specific tasks in the e-commerce field. In this paper, we present a brief survey of machine learning and deep learning techniques in the context of e-commerce, focusing on the years 2018–2023 in a Google Scholar search, with the aim of identifying state-of-the-art approaches, main topics, and potential challenges in the field. We first introduce the applied machine learning and deep learning techniques, spanning from support vector machines, decision trees, and random forests to conventional neural networks, recurrent neural networks, generative adversarial networks, and beyond. Next, we summarize the main topics, including sentiment analysis, recommendation systems, fake review detection, fraud detection, customer churn prediction, customer purchase behavior prediction, prediction of sales, product classification, and image recognition. Finally, we discuss the main challenges and trends, which are related to imbalanced data, over-fitting and generalization, multi-modal learning, interpretability, personalization, chatbots, and virtual assistance. This survey offers a concise overview of the current state and future directions regarding the use of machine learning and deep learning techniques in the context of e-commerce. Further research and development will be necessary to address the evolving challenges and opportunities presented by the dynamic e-commerce landscape.

Deep-SDM: A Unified Computational Framework for Sequential Data Modeling Using Deep Learning Models

“…Deep learning architectures that can process vast amounts of data, recognize patterns, and make accurate predictions have opened up new possibilities across various sectors, leading to increased efficiency, improved decision-making, and enhanced user experiences. It has revolutionized many industries, including manufacturing [10][11][12], finance [13,14], healthcare [15][16][17][18], environment [19], electronics [20], energy [21,22], agriculture [23,24], transportation [25,26], entertainment [27,28], retail [29,30], e-commerce [31,32], and many others, transforming the way we approach complex tasks and unlocking new possibilities. Although it is a relatively new and emerging technology, many data-driven or rule-based algorithms, from naive to complex, are already employed in various scientific fields [6,[33][34][35][36][37][38][39].…”

Development of a Time Series E-Commerce Sales Prediction Method for Short-Shelf-Life Products Using GRU-LightGBM

“…In recent years, e-commerce has entered a new stage of rapid expansion [1][2][3]. The development and prosperity of e-commerce mean that it has replaced and eliminated many offline stores.…”

Optimizing Sentiment Analysis Models for Customer Support: Methodology and Case Study in the Portuguese Retail Sector

“…Sentiment analysis (SA) [1] is helpful in natural language processing (NLP), underpinning decision-making processes across various sectors such as commerce, finance, healthcare, and product analysis [2]. Machine learning (ML) [3] has been employed in various sectors for supporting the decision-making, including biomedical and healthcare data [4,5], data privacy [6], and forecasting [7].…”