Towards Personalized Movie Selection for Wellness: Investigating Event-Inspired Movies

Chu, Sharon Lynn; Brown, Sarah Anne; Park, Hannah; Spornhauer, Brittnie

doi:10.1080/10447318.2020.1768665

Cited by 6 publications

(5 citation statements)

References 44 publications

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“…It can be seen that no distinct behavior patterns are evident in consumer behavior and this also coincides with the situational argumentation of [18] that we must always consider the context. This could also justify the argument of a day-based recommendation [35]. The hedonist may pursue meaningful experiences beyond his orientation towards pleasure and relaxation, which he seeks in entertainment content such as books and movies.…”

Section: Discussionmentioning

confidence: 97%

“…One contribution shows that with the help of modality-based algorithms, the hedonic or eudaimonic quality of a movie (annotated by users) can be predicted [8]. Anothers contribution suggests allocating movie recommendations according to users' everyday well-being [35]. Above all, it emphasizes the situational character of entertainment, that we develop different needs in our everyday lives and therefore require different stimuli daily.…”

Section: Related Workmentioning

confidence: 99%

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

Joers,

De Luca

2024

Preprint

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Human-Computer Interaction (HCI) is undergoing a paradigm shift toward placing well-being at the center of technology development. Information systems (IS) research is called upon to align business goals with different well-being orientations, particularly those of hedonic and eudaimonic individuals. While hedonic-oriented individuals choose relaxation and pleasure as their goals, eudaimonic-oriented individuals are interested in meaningful experiences that go beyond pleasure. Surprisingly, there is a lack of datasets provided for recommender systems (RS) considering well-being orientations. We developed and thus present the first public dataset in this paper that was created based on an online survey with 229 participants. Respondents were asked to name their favorite movies and books and then the participants were clustered into two orientations using the HEMA-Revised (HEMA-R) Scale. In total, we collected 1,563 items (799 movies and 764 books). The dataset is intended to contribute to the improvement of (group) RS architectures.

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Section: Discussionmentioning

confidence: 97%

Section: Related Workmentioning

confidence: 99%

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

Joers,

De Luca

2024

Preprint

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“…The graphical representations of emotions in the IFelt system [75,76] or in the MovieClouds [77] have been proposed to help viewers navigate the affective states presented and elicited by movies. Other examples of this approach are Topal and Ozsoyoglu [78] emotional maps, Mokryn et al [79] emotional signatures, and Chu et al [8] "event inspired movie recommendation system," which offers movie recommendations based on users' daily events and previously identified life goals and values.…”

Section: Conceptual Approaches Of Emotionsmentioning

confidence: 99%

“…Nowadays, it is possible to find some attempts that combine user's daily subjective experience (e.g., affect, values) and movie search, navigation, and recommendation, in an attempt to help emotion regulation and life satisfaction (e.g., [7,8]). There is not, however, a common method or theory to these approaches, which compromises results' clarity and replicability.…”

Section: Introductionmentioning

confidence: 99%

Searching, Navigating, and Recommending Movies through Emotions: A Scoping Review

Piçarra

Reis

Chambel

et al. 2022

Human Behavior and Emerging Technologies

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Movies offer viewers a broad range of emotional experiences, providing entertainment, and meaning. Following the PRISMA-ScR guidelines, we reviewed the literature on digital systems designed to help users search and browse movie libraries and offer recommendations based on emotional content. Our search yielded 83 eligible documents (published between 2000 and 2021). We identified 22 case studies, 34 empirical studies, 26 proof of concept, and one theoretical paper. User transactions (e.g., ratings, tags) were the preferred source of information. The documents examined approached emotions from both a categorical ( n = 35 ) and dimensional ( n = 18 ) perspectives, and nine documents offer a combination of both approaches. Although there are several authors mentioned, the references used are frequently dated, and 12 documents do not mention author or model used. We identified 61 words related to emotion or affect. Documents presented on average 1.36 positive terms and 2.64 negative terms. Sentiment analysis ( n = 31 ) is frequently used for emotion identification, followed by subjective evaluations ( n = 15 ), movie low-level audio and visual features (n = 11), and face recognition technologies ( n = 8 ). We discuss limitations and offer a brief review of current emotion models and research.

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“…Few explanation types are based on the context of users. One example of this type of context-based explanations can be fashioned from the work of Chu et al (2020), who presented a movie recommendation approach based on events that occur in the user's daily life. In their approach, movies are recommended based on how well they match with characteristics of significant events reported by the user.…”

Section: Background and Related Work Explanation Typesmentioning

confidence: 99%

Understanding the Effects of Explanation Types and User Motivations on Recommender System Use

Chu

Rao

et al. 2020

HCOMP

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It is becoming increasingly common for intelligent systems, such as recommender systems, to provide explanations for their generated recommendations to the users. However, we still do not have a good understanding of what types of explanations work and what factors affect the effectiveness of different types of explanations. Our work focuses on explanations for movie recommender systems. This paper presents a mixed study where we hypothesize that the type of explanation, as well as user motivation for watching movies, will affect how users respond to recommendation system explanations. Our study compares three types of explanations: i) neighbor-ratings, ii) profile-based, and iii) event-based, as well as three types of user movie-watching motivations: i) hedonic (fun and relaxation), ii) eudaimonic (inspiration and meaningfulness), and iii) educational (learning new content). We discuss the implications of the study results for the design of explanations for movie recommender systems, and future novel research directions that the study results uncover.

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Towards Personalized Movie Selection for Wellness: Investigating Event-Inspired Movies

Cited by 6 publications

References 44 publications

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

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

Searching, Navigating, and Recommending Movies through Emotions: A Scoping Review

Understanding the Effects of Explanation Types and User Motivations on Recommender System Use

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