本研究针对20至30岁的生活在中国大城市的青年群体，研究如何使用人工智能技术的自然语言处理和机器学习提供更为简便易用的界面，使用户能够更轻松、更方便地规划旅游出行和娱乐休闲活动。文章通过市场调查和文献综述，详细分析了目标用户的特定需求以及当前相关休闲服务类软件的不足。现有服务类软件在简洁方便、个性化和互动性等方面难以满足年轻用户的期望。为解决这些问题，本研究提出借助人工智能来改进和优化娱乐信息服务设计的新概念，结合自然语言处理和机器学习技术，实时分析和理解用户的偏好和行为模式，从而提供个性化的出行建议。研究分析了人工智能对优化服务设计界面的优势，探讨了如何利用AI技术进行用户数据分析和定制推荐系统。设计过程运用了双钻模型，论述了作者在发现、定义、开发和转达四个阶段的工作成果。设计前期运用了定量和定性分析方法，对130名参与者进行在线问卷调查和对12名用户进行深入访谈，收集了与本研究相关的具体用户数据，基于这些数据构建了详细的用户画像、服务蓝图、用户旅程图和应用软件界面设计。本研究还通过伪代码形式提出了人工智能的应用策略，展示了如何使用自然语言处理和机器学习等人工智能技术来加强个性化服务的路径。最后，对设计原型进行了可用性评估，以验证应用软件界面设计的效率和用户接受度。测试结果表明，这种基于人工智能的服务设计在提供个性化服务方面大大提高了用户满意度，有效减少了用户在多平台筛选信息的困扰，提升了用户体验。综上所述，文章展示了运用人工智能优化信息服务设计的思考过程、方法和路径，为服务设计与人工智能技术的结合提供了新视角。
This thesis “Research on AI Integration in Entertainment Information Service Design” explores how to utilize artificial intelligence technology to recommend leisure plans for urban youths aged 20-30. Specifically, the research employs natural language processing and machine learning within AI to enhance the personalization of services, facilitating easier and more convenient planning and enjoyment of leisure activities for users. As urbanization intensifies, urban youths, especially, face increasing life pressures and the challenges of an accelerated pace of life in planning and implementing leisure activities. Consequently, this study targets the 20-30 age group in modern urban life, addressing the challenges of information overload and fast-paced living faced by young people. It proposes AI solutions using pseudocode and Python to optimize leisure planning services to enhance user satisfaction.The research begins with market surveys and literature reviews to analyze in detail the current shortcomings of leisure services and the specific needs of the target users. The study indicates that due to deficiencies in personalization and interaction within existing services, it is challenging to meet the expectations of young users. To address these issues, a new AI-based service design model is proposed, integrating natural language processing and machine learning technologies to analyze and understand user preferences and behavioral patterns in real-time, thus offering customized leisure activity suggestions. The theoretical exploration revisits the definitions of service design and the fundamental concepts of natural language processing and machine learning, discussing how these technologies can be applied to analyze user data and build customized recommendation systems. Moreover, during the service design process, this study employs the Double Diamond model, clarifying the four stages of discovery, definition, development, and delivery. Utilizing this model, the design process systematically progresses from problem definition to solution proposal, ensuring the service design suggestions are user-centered and target-oriented. The research methodology combines quantitative and qualitative methods, collecting specific user data related to this study through online surveys of 130 participants and in-depth interviews with 12 individuals. This data aids in constructing detailed personas, service blueprints, user journey maps, and application prototypes within service design. Additionally, the study presents AI application strategies in the form of pseudocode, specifically illustrating how AI technologies such as natural language processing and machine learning can be used to enhance service personalization. Lastly, usability assessments are conducted to verify the efficiency and user acceptance of the designed service. Test results show that this AI-based service design significantly enhances user satisfaction, especially in providing personalized services, and it effectively reduces the information burden on users during the selection of leisure activities, thereby enhancing user experience.In summary, this research demonstrates how artificial intelligence can effectively function in enhancing service design, improving service efficiency, and user satisfaction. It offers new perspectives and methodologies for integrating service design with artificial intelligence technology. The insights and findings of this study are expected to provide valuable references for future efforts in similar service designs.
