This paper presents an innovative approach to address the problems researchers face in Emotion Aware Recommender Systems (EARS): the difficulty and cumbersome collecting voluminously good quality emotion-tagged datasets and an effective way to protect users' emotional data privacy. Without enough good-quality emotion-tagged datasets, researchers cannot conduct repeatable affective computing research in EARS that generates personalized recommendations based on users' emotional preferences. Similarly, if we fail to fully protect users' emotional data privacy, users could resist engaging with EARS services. This paper introduced a method that detects affective features in subjective passages using the Generative Pre-trained Transformer Technology, forming the basis of the Affective Index and Affective Index Indicator (AII). Eliminate the need for users to build an affective feature detection mechanism. The paper advocates for a separation of responsibility approach where users protect their emotional profile data while EARS service providers refrain from retaining or storing it. Service providers can update users' Affective Indices in memory without saving their privacy data, providing Affective Aware recommendations without compromising user privacy. This paper offers a solution to the subjectivity and variability of emotions, data privacy concerns, and evaluation metrics and benchmarks, paving the way for future EARS research.

翻译：本文提出了一种创新方法，以应对情感感知推荐系统（EARS）研究者面临的问题：收集大量高质量情感标注数据集的困难和繁琐性，以及有效保护用户情感数据隐私的方式。若缺乏足够高质量的情感标注数据集，研究者便无法基于用户情感偏好生成个性化推荐，从而无法在EARS中开展可重复的情感计算研究。同样，若未能充分保护用户情感数据隐私，用户可能会抵触使用EARS服务。本文介绍了一种利用生成式预训练Transformer技术检测主观文本情感特征的方法，构建了情感指数及情感指数指标（AII）的基础，消除了用户构建情感特征检测机制的需求。本文倡导责任分离方法：用户保护自身情感档案数据，而EARS服务提供商不保留或存储这些数据。服务提供商可在无需保存隐私数据的前提下，于内存中更新用户的情感指数，从而在不损害用户隐私的情况下提供情感感知推荐。本文针对情感的主观性与多变性、数据隐私问题以及评估指标与基准提出了解决方案，为未来EARS研究铺平了道路。