Authors:
John Kalung Leung
1
;
Igor Griva
2
and
William G. Kennedy
3
Affiliations:
1
Computational and Data Sciences Department, Computational Sciences and Informatics, College of Science, George Mason University, 4400 University Drive, Fairfax, Virginia 22030, U.S.A.
;
2
Department of Mathematical Sciences, MS3F2, Exploratory Hall 4114, 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.
Keyword(s):
Affective Computing, Affective Aware Top-N Recommendations, Deep Learning, Text-based Emotion Mining.
Abstract:
This paper investigates the causality in the decision making of movie recommendations through the users' affective profiles. We advocate a method of assigning emotional tags to a movie by the auto-detection of the affective features in the movie's overview. We apply a text-based Emotion Detection and Recognition model, which trained by tweets short messages and transfers the learned model to detect movie overviews’ implicit affective features. We vectorize the affective movie tags to represent the mood embeddings of the movie. We obtain the user's emotional features by taking the average of all the movies' affective vectors the user has watched. We apply five-distance metrics to rank the Top-N movie recommendations against the user's emotion profile. We found Cosine Similarity distance metrics performed better than other distance metrics measures. We conclude that by replacing the top-N recommendations generated by the Recommender with the reranked recommendations list made by the Co
sine Similarity distance metrics, the user will effectively get affective aware top-N recommendations while making the Recommender feels like an Emotion Aware Recommender.
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