Research on Multimodal Prediction of E-Commerce Customer Satisfaction Driven by Big Data DOI Creative Commons
Xiaodong Zhang, Chunrong Guo

Applied Sciences, Год журнала: 2024, Номер 14(18), С. 8181 - 8181

Опубликована: Сен. 11, 2024

This study deeply integrates multimodal data analysis and big technology, proposing a learning framework that consolidates various information sources, such as user geographic location, behavior data, product attributes, to achieve more comprehensive understanding prediction of consumer behavior. By comparing the performance unimodal approaches in handling complex cross-border e-commerce it was found models using Adam optimizer significantly outperformed traditional terms accuracy loss rate. The improvements were particularly notable training testing accuracy. demonstrates efficiency superiority methods capturing analyzing heterogeneous data. Furthermore, explores validates potential enhance customer satisfaction environment. Based on core findings, specific applications technology operations further explored. A series innovative strategies aimed at improving operational efficiency, enhancing satisfaction, increasing global market competitiveness proposed.

Язык: Английский

Digitally Enhanced World of Realities DOI
Tunde Toyese Oyedokun

Advances in business strategy and competitive advantage book series, Год журнала: 2025, Номер unknown, С. 37 - 70

Опубликована: Янв. 3, 2025

The digital age has seen an unparalleled evolution in technology, particularly with immersive technologies like augmented reality (AR), virtual (VR), and mixed (MR), leading us toward a metaverse-defined future. This chapter explores their transformative impact on various industries as we stand the brink of convergence between physical realms. Tracing historical trajectory pivotal milestones AR VR development, it examines current state domains AI IoT. Examining applications across sectors such healthcare, education, retail, unveils innovative business models emerging within metaverse. From redefining consumer behavior e-commerce to revolutionizing traditional learning methods, are reshaping necessitate understanding for individuals, businesses, policymakers navigating this landscape.

Язык: Английский

Процитировано

0
Research on Multimodal Prediction of E-Commerce Customer Satisfaction Driven by Big Data DOI Creative Commons
Xiaodong Zhang, Chunrong Guo

Applied Sciences, Год журнала: 2024, Номер 14(18), С. 8181 - 8181

Опубликована: Сен. 11, 2024

This study deeply integrates multimodal data analysis and big technology, proposing a learning framework that consolidates various information sources, such as user geographic location, behavior data, product attributes, to achieve more comprehensive understanding prediction of consumer behavior. By comparing the performance unimodal approaches in handling complex cross-border e-commerce it was found models using Adam optimizer significantly outperformed traditional terms accuracy loss rate. The improvements were particularly notable training testing accuracy. demonstrates efficiency superiority methods capturing analyzing heterogeneous data. Furthermore, explores validates potential enhance customer satisfaction environment. Based on core findings, specific applications technology operations further explored. A series innovative strategies aimed at improving operational efficiency, enhancing satisfaction, increasing global market competitiveness proposed.

Язык: Английский

Процитировано

1