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论文编号：	15187
作者编号：	2320224118
上传时间：	2024/12/11 20:27:56
中文题目：	面向用户需求的智能家居场景推荐策略研究及应用
英文题目：	Research and application of smart home scene recommendation strategies oriented to user needs
指导老师：	姚欣林 副教授
中文关键字：	场景推荐；智能家居；个性化推荐；推荐策略；
英文关键字：	Scene recommendation; smart home; personalized recommendation; recommendation strategy;
中文摘要：	近年来，伴随物联网、大数据及人工智能技术的迅猛进步，智能家居行业获得了新的发展契机。本研究以H公司为例，探讨基于用户个性化行为的场景推荐策略。通过分析智能家居行业的现状和发展趋势，并结合STP等分析工具，全面评估H公司在当前市场环境中的地位与挑战。并且本文也深入分析了H公司当前的场景推荐现状，精准地识别了其中存在的问题。 基于上述所有的分析结果，本文提出了一套基于用户个性化行为进行场景动作推荐的策略，并将此策略结合场景推荐形成综合的解决方案。该方案不仅涵盖了用户信息收集、用户标签体系构建，还覆盖了用户行为分析的全过程。通过多渠道数据收集、用户标签构建、大数据分析和机器学习算法，为用户推荐最适合的动作和场景。此外，本文还对方案部分上线后的数据表现进行了详细分析，基于数据的分析结果反馈给提出了公司后续的发展建议。根据本文研究结果显示，基于用户个性化行为的场景推荐策略能够有效提升用户体验，增强用户粘性，显著提升用户体验和产品竞争力。为智能家居的部分企业带来全新的发展机会和思路。 本研究不仅为H公司的智能家居场景推荐提供了理论指导和实践参考，优化智能家居场景推荐的具体建议，也为整个智能家居行业的场景推荐策略优化提供了新的思路。
英文摘要：	In recent years， with the rapid progress of the Internet of Things， big data and artificial intelligence technology， the smart home industry has gained new development opportunities. This thesis takes Company H as an example to explore scenario recommendation strategies based on user personalized behavior. By analyzing the current status and development trends of the smart home industry， and combiningSTP， we comprehensively evaluate Company H's position and challenges in the current market environment. Moreover， this thesis also conducts an in-depth analysis of Company H’s current scene recommendation status and accurately identifies the existing problems. Based on all the above analysis results， this thesis proposes a strategy for scene action recommendation based on user personalized behavior， and combines this thesis with scene recommendation to form a comprehensive solution. This solution not only covers user information collection and user tag system construction， but also covers the entire process of user behavior analysis. Through multi-channel data collection， user tag construction， big data analysis and machine learning algorithms， the most suitable actions and scenarios are recommended for users. In addition， this thesis also conducts a detailed analysis of the data performance after the program part was launched， and based on the data analysis results， the company's subsequent development suggestions were put forward. According to the research results of this thesis， scenario recommendation strategies based on user personalized behavior can effectively improve user experience， enhance user stickiness， and significantly improve user experience and product competitiveness. It brings new development opportunities and ideas to some smart home companies. This thesis not only provides theoretical guidance and practical reference for Company H's smart home scene recommendation， but also provides specific suggestions for optimizing smart home scene recommendation. It also provides new ideas for optimizing the scene recommendation strategy for the entire smart home industry.
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