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基于机器学习的移动互联网产品交互等待体验设计评估

Evaluation on interactive waiting experience design of mobile internet products based on machine learning.

作者信息

Yu Yi

机构信息

Artistics, Krirk University, Bangkhen, Bangkok, 10220, Thailand.

出版信息

Sci Rep. 2023 Oct 9;13(1):16985. doi: 10.1038/s41598-023-43405-2.

DOI:10.1038/s41598-023-43405-2
PMID:37813893
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10562367/
Abstract

In today's rapidly changing economy, efficient lifestyle has become the current situation of most mobile product users. With the development of performance tools and technologies, a fast lifestyle has brought more wealth and opportunities to users. The slow pace and fluctuating time are indirect income losses, which cause user anxiety to some extent. When the waiting time exceeds the user's waiting threshold, users would experience negative emotions, such as boredom, anxiety and anger, and product satisfaction would drop significantly. Therefore, by analyzing the uniqueness of mobile Internet products and the characteristics of users, this paper studied the reasons and influencing factors of product interactive waiting, and then used machine learning algorithm to analyze the cost function of interactive waiting experience. Finally, the corresponding interactive waiting experience design strategy was proposed. By comparison, the user experience after product interaction optimization design was 8.4% higher than that before product interaction optimization design, and the user frequency was also 14.7% higher after optimization design. In short, user experience plays an important role in product interaction design.

摘要

在当今快速变化的经济环境中,高效生活方式已成为大多数移动产品用户的现状。随着性能工具和技术的发展,快速生活方式给用户带来了更多财富和机遇。节奏缓慢和时间波动会造成间接的收入损失,在一定程度上引发用户焦虑。当等待时间超过用户的等待阈值时,用户会体验到诸如无聊、焦虑和愤怒等负面情绪,产品满意度会大幅下降。因此,本文通过分析移动互联网产品的独特性和用户特征,研究了产品交互等待的原因及影响因素,进而运用机器学习算法分析交互等待体验的成本函数。最后,提出了相应的交互等待体验设计策略。通过对比,产品交互优化设计后的用户体验比优化设计前高出8.4%,优化设计后的用户频次也高出14.7%。简而言之,用户体验在产品交互设计中起着重要作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6997/10562367/c40b64bc069e/41598_2023_43405_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6997/10562367/56829f1b161f/41598_2023_43405_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6997/10562367/084a6a71d548/41598_2023_43405_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6997/10562367/e4b8e0e40eaf/41598_2023_43405_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6997/10562367/558586e962e4/41598_2023_43405_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6997/10562367/a6d11c7c5f49/41598_2023_43405_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6997/10562367/46331b28a917/41598_2023_43405_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6997/10562367/c40b64bc069e/41598_2023_43405_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6997/10562367/56829f1b161f/41598_2023_43405_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6997/10562367/084a6a71d548/41598_2023_43405_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6997/10562367/e4b8e0e40eaf/41598_2023_43405_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6997/10562367/558586e962e4/41598_2023_43405_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6997/10562367/a6d11c7c5f49/41598_2023_43405_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6997/10562367/46331b28a917/41598_2023_43405_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6997/10562367/c40b64bc069e/41598_2023_43405_Fig7_HTML.jpg

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