大数据驱动的用户体验设计综述

目的研究大数据技术在用户体验设计领域的理论研究与实践应用。方法通过国内外相关文献研究和各行业对大数据驱动用户体验优化设计实践应用的分析,总结归纳当前典型的应用场景,分析在大数据时代用户体验研究将发生的变革和未来发展的趋势。结论系统地探讨了大数据在用户体验设计领域的主要应用场景以及实现方法,重点阐述了内容推荐、用户画像、需求分析、可用性分析和智能判断与决策五个应用场景,并提供了相应的参考框架。针对当下的理论研究和实践经验,提出了大数据驱动的用户体验研究未来,将会成为用户体验研究主要方法的趋势,其研究方法还将从单一的定量研究,转变为定量与定性研究相结合,更多维度的社会化大数据将逐步得到广泛应用,涉及用户隐私的数据安全与隐私侵犯问题,也将逐步得到重视。人工智能技术与大数据技术的结合,将进一步扩展用户体验研究的应用范围,深度学习、认知计算、社会计算等新技术,将逐步覆盖传统大数据用户体验研究领域无法解决的问题。

Big Data-Driven User Experience Design

The work aims to study the theoretical research and practical application of big data technology in the field of user experience design. Through the research on related literature at home and abroad and the analysis on practical application of big data-driven user experience optimal design in different industries, the typical application scenario was summarized and the change and development of user experience design in the big data era was analyzed. The application scenario and implementation methods of big data-driven user experience design are discussed systematically. 5 application scenarios including recommendation system, user portrait, requirement analysis, availability analysis and intelligent decision-making are clarified emphatically and those reference frameworks are provided. According to the current theoretical research and practical experience, it is proposed that the research on big data-driven user experience will become the trend of the main methods of user experience research in the future. The research method will also be changed from a single quantitative research to a combination of quantitative and qualitative research. More dimensions of social big data will be used widely, and data security and privacy infringement issues involving user privacy will also be gradually paid attention to. The combination of artificial intelligence technology and big data technology will further expand the application scope of user experience research. New technologies such as in-depth learning, cognitive computing and social computing will gradually cover problems that cannot be solved in the traditional big data user experience research field.