Authors:
John Leung
1
;
Igor Griva
2
;
William Kennedy
3
;
Jason Kinser
4
;
Sohyun Park
1
and
Seo Lee
5
Affiliations:
1
Computational Sciences and Informatics, Computational and Data Sciences Department, George Mason University Korea, 119-4 Songdomunhwa-ro, Yeonsu-gu, Incheon, 21985, Korea
;
2
Department of Mathematical Sciences, George Mason University,4400 University Drive, Fairfax, Virginia 22030, U.S.A.
;
3
Center for Social Complexity, Computational and Data Sciences Department, College of Science, George Mason University, 4400 University Drive, Fairfax, Virginia 22030, U.S.A.
;
4
Computational Sciences and Informatics, Computational and Data Sciences Department, College of Science, George Mason University, 4400 University Drive, Fairfax, Virginia 22030, U.S.A.
;
5
Department of Communications, George Mason University Korea, 119-4 Songdomunhwa-ro, Yeonsu-gu, Incheon, 21985, Korea
Keyword(s):
Emotion Aware Recommender Systems, Affective Computing, Users and Items Emotion Profiles, Text-Based Emotion Detection and Recognition, Affective Indices and Affective Index Indicators, Emotion Identification.
Abstract:
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 goodquality 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 protect users’ emotional data privacy fully, 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 th
eir 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.
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