  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.
