arXiv:2007.00636v1 [cs.IR] 1 Jul 2020 Making Use of Affective Features from Media Content Metadata for Better Movie Recommendation Making John Kalung Leung 1 , Igor Griva 2 and William G. Kennedy 3 a 1 Computational and Data Sciences Department, Computational Sciences and Informatics, College of Science, George Mason University, 4400 University Drive, Fairfax, Virginia 22030, USA 2 Department of Mathematical Sciences, MS3F2, Exploratory Hall 4114, George Mason University,4400 University Drive, Fairfax, Virginia 22030, USA 3 Center for Social Complexity, Computational and Data Sciences Department, College of Science, George Mason University, 4400 University Drive, Fairfax, Virginia 22030, USA {jleung2, igriva, wkennedy}@gmu.edu Keywords: context-aware, emotion mining, affective computing, recommender systems, movie recommendations, deep learning Abstract: Our goal in this paper aims to investigate the causality in the decision making of movie recommendations from a Recommender perspective through the behavior of users’ affective moods. We illustrate a method of assigning emotional tags to a movie by auto-detection of the affective attributes in the movie overview. We apply a text-based Emotion Detection and Recognition model, which trained by the short text of tweets, and then transfer the model learning to detect the implicit affective features of a movie from the movie overview. We vectorize the affective movie tags through embedding to represent the mood of the movie. Whereas we vectorize the user’s emotional features by averaging all the watched movie’s vectors, and when incorporated the average ratings from the user rated for all watched movies, we obtain the weighted vector. We apply the distance metrics of these vectors to enhance the movie recommendation making of a Recommender. We demonstrate our work through an SVD based Collaborative Filtering (SVD-CF) Recommender. We found an improved 60% support accuracy in the enhanced top-5 recommendation computed by the active test user distance metrics versus 40% support accuracy in the top-5 recommendation list generated by the SVD-CF Recommender. 1 Introduction Movie recommendations come from different sources. A more traditional way to make a movie recommendation is by word of mouth through moviegoers who have watched the movie, or relying on elite movie critics who wrote about their opinions of the film, or through news media, publications, and advertisements. Since the dawn of the Internet era in the last century, we rely on machine automation to make movie recommendations using various Rec- ommender methodologies (Bobadilla et al., 2013), (Zhang et al., 2011), (Scheel et al., 2012), and (Kompan and Bielikova, 2014). More recently, we have applied the more advanced Deep Learning (DL) techniques in Recommenders (Zhang et al., 2017). After a century, the field in recommendation making is still in active research (Jannach et al., 2010). a https://orcid.org/0000-0001-9238-1215 Regardless of the efforts, we have invested in Rec- ommenders research, and they always seem to be more ways to make improvements even in the field (Beel et al., 2015). In this paper, we shall include primary human emotions as an aspect of making movie recommendations through a Recommender (Canales and Mart´ ınez-Barco, 2014). Emotion affects human experience and influences our daily activities on all levels of the decision-making process. When a user ponders over a list of recom- mended items such as songs, books, movies, prod- ucts, or services, his affective state of preferences in- fluences his decision making on which recommended item he chooses to consume. Emotion plays a role in our decision-making process in preference selection (Naqvi et al., 2006). However, up to now, informa- tion retrieval (IF) and Recommender Systems (RS) give little attention to include human emotion as a source of user context (Ho and Tagmouti, 2006). Our goal is to make affective awareness a component of