TY -的A2吴Chia-Huei盟——刘,力平PY - 2022 DA - 2022/04/25 TI -基于机器学习技术的电子商务个性化推荐SP - 1761579六世- 2022 AB -电子商务为用户提供了越来越多的选择,其结构变得越来越复杂。不可避免的是,它带来了信息过载的问题。此问题的解决方案是使用机器学习技术电子商务个性化推荐系统。人们常常显得困惑当面对广泛的信息,不能抓住要点。本文研究了电子商务个性化推荐技术:深入分析电子商务推荐系统的相关技术和算法,提出了最新的电子商务推荐系统的架构根据电子商务推荐系统的发展现状。系统推荐准确性和实时性要求,将系统划分为两个部分:离线采矿和在线建议和分析,并实现了各部分的功能和技术。基于用户的推荐系统,协同过滤推荐系统和基于内容的推荐系统进行了分析,分别。个性化推荐不能仅仅快速帮助客户找到所需的商品信息在一个广泛的复杂的信息,还可以比较更多的商品信息,帮助客户判断。然而,现有的推荐系统有一些问题,如缺乏个性推荐,推荐的相关性,穷人及时性的推荐。最后,推荐系统相结合三个推荐算法设计,并进行实验。 The newly designed recommendation system is compared with three different recommendation systems, and a summary and outlook are made. Based on the introduction of the relevant theories, characteristics, and mainstream technologies of personalized recommendation based on machine learning, this document presents a constructive example of a model based on the factors that influence personalized e-commerce information recommendations in the retail sector. Through questionnaire surveys, we analyze and design the influencing factors for consumers to purchase personalized products after the survey and build a project using state-of-the-art field learning techniques. Through the model to test the eight hypotheses proposed in this paper, the results show that customer income level, customer online shopping experience, commodity prices, product quality, recommendation relevance, credit evaluation, and service quality will have a significant positive impact on shopping willingness and ultimately affect the customer’s shopping behavior. e-commerce platform can use this influencing factor to establish personalized information recommendation service mode. SN - 1574-017X UR - https://doi.org/10.1155/2022/1761579 DO - 10.1155/2022/1761579 JF - Mobile Information Systems PB - Hindawi KW - ER -