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.

中文翻译：

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电子商务研究中的机器学习和深度学习技术简介

电子商务的快速增长显着增加了对解决电子商务领域特定任务的先进技术的需求。在本文中，我们对电子商务背景下的机器学习和深度学习技术进行了简要调查，重点关注谷歌学术搜索中的 2018-2023 年，旨在确定最先进的方法、主要主题以及该领域的潜在挑战。我们首先介绍应用的机器学习和深度学习技术，从支持向量机、决策树和随机森林到传统神经网络、循环神经网络、生成对抗网络等。接下来，我们总结主要主题，包括情感分析、推荐系统、虚假评论检测、欺诈检测、客户流失预测、客户购买行为预测、销售预测、产品分类和图像识别。最后，我们讨论了主要的挑战和趋势，这些挑战和趋势与数据不平衡、过度拟合和泛化、多模式学习、可解释性、个性化、聊天机器人和虚拟协助有关。这项调查简要概述了在电子商务背景下使用机器学习和深度学习技术的现状和未来方向。需要进一步的研究和开发来应对动态电子商务环境带来的不断变化的挑战和机遇。