Eudaimonic Modeling of Moviegoers

Abstract

One of the important aspects of movie-making is to trigger emotional responses in viewers. These emotional experiences can be divided into hedonic and eudaimonic. While the former are characterized as plain enjoyment, the latter deal with getting greater insight, self-reflection or contemplation. So far, modeling of user preferences about movies and personalization algorithms have largely ignored the eudaimonic aspect of the consumption of movies. In this paper we fill this gap by exploring what are the relationship between (i) eudaimonic and hedonic characteristics of movies, (ii) users' preferences and (iii) users' personality. Our results show that eudaimonic user profiling effectively divides users into pleasureseekers and meaning-seekers.

Considering Well-being Orientation: the Eudaimonic and Hedonic Dataset for Well-being-centered Recommender Systems

“…This valid separation is already in use in a large number of distributions in various domains [19,20,21,13,8,22,23,24,25,26,27,28,29,30,31,32]. For example, RS for movies and related watch behavior have been studied, with eudaimonic quality linked to the meaningful experiences and hedonic quality associated with moments of pleasure [13,8,16,15]. With the help of a questionnaire set for hedonic or eudaimonic movie consumption, preferences can be identified and categorized into one of the categories (hedonia or eudaimonia) [13].…”

“…However, current RS still lack effectiveness [12] and therefore we want to propose a novel approach that enables grouping and recommending by well-being orientation. The possibility of taking well-being orientations into account has been positively demonstrated in initial applications [13,8,14,15,16]. On the basis of the analysis of past research, we identified the need to contribute to item-based group RS in the following way: analyzing differences between eudaimonic-and hedonic-oriented consumption behavior (clustering), aggregating items in each group (aggregation), and test accurate recommendations (evaluation).…”

Editorial: Psychological Models for Personalized Human-Computer Interaction (HCI)

“…Recent work has thus started to adopt a more theory-driven approach by including psychological theories and models to improve personalized systems (see for an overview; Graus and Ferwerda, 2019 ). These systems take advantage of psychological theories/models, such as emotions (Tkalčič et al, 2013b ; Tkalčič and Ferwerda, 2018 ), personality (Ferwerda et al, 2017 ; Wu et al, 2018 ), skills (Ferwerda and Graus, 2018 ), and culture (Schedl et al, 2017 ) to explain and predict behaviors of users. This allows for a deeper understanding of users' behavior, preferences, and needs, which in turn also lead to more generalizable results.…”