基于用户情绪设计推荐系统，提升个性化体验
Emotion-based Recommender System
- 提出新理论与指标，量化用户交互时的情绪变化
- 开发可视化技术，展示用户情绪在生命周期中的演变
- 构建情绪驱动推荐框架，实验验证有效性
推荐系统是亚马逊、TikTok等大型互联网公司核心技术之一。尽管全球每天有数百万用户使用推荐系统，且已有大量数据分析用于提升系统技术精度，但目前对用户在使用过程中情绪状态的关注仍极为有限。本文提出一种新理论与度量方法，用于捕捉用户在与推荐系统交互时的情绪表现，并提供高效可视化技术，展示用户情绪在其生命周期内的变化过程。最后，我们设计了一个基于情绪的推荐算法框架，并通过一个直观示例和实验结果，验证了该理论的有效性。
原文摘要 · Abstract (English)
Recommender system is one of the most critical technologies for large internet companies such as Amazon and TikTok. Although millions of users use recommender systems globally everyday, and indeed, much data analysis work has been done to improve the technical accuracy of the system, to our limited knowledge, there has been little attention paid to analysis of users' emotion in recommender systems. In this paper, we create a new theory and metrics that could capture users' emotion when they are interacting with recommender systems. We also provide effective and efficient visualization techniques for visualization of users' emotion and its change in the customers' lifetime cycle. In the end, we design a framework for emotion-based recommendation algorithms, illustrated in a straightforward example with experimental results to demonstrate the effectiveness of our new theory.
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