=Paper=
{{Paper
|id=Vol-3815/paper9
|storemode=property
|title=Intended Movie Experience: Linking Elicited Emotions to Eudaimonic and Hedonic Characteristics
|pdfUrl=https://ceur-ws.org/Vol-3815/paper9.pdf
|volume=Vol-3815
|authors=Arsen Matej Golubovikj,Osnat Mokryn,Marko Tkalčič
|dblpUrl=https://dblp.org/rec/conf/intrs/GolubovikjMT24
}}
==Intended Movie Experience: Linking Elicited Emotions to Eudaimonic and Hedonic Characteristics==
https://ceur-ws.org/Vol-3815/paper9.pdf
Intended Movie Experience: Linking Elicited Emotions to
Eudaimonic and Hedonic Characteristics
Arsen Matej Golubovikj1 , Osnat Mokryn2 and Marko Tkalčič1
1
University of Primorska, Faculty of Mathematics, Natural Sciences and Information Technologies Glagoljaška 8, SI-6000 Koper,
Slovenia
2
University of Haifa, Department of Information Systems, Haifa, Israel
Abstract
This study investigates the relationship between movies’ elicited emotions and their eudaimonic (meaningful) and
hedonic (pleasurable) characteristics. We use emotional signatures derived from movie reviews, which have been
previously shown to capture these elicited emotions. We examine correlations with both the movies’ eudaimonic
and hedonic characteristics and the users’ eudaimonic and hedonic orientations, calculated based on their highly
rated movies. We demonstrate the predictive power of emotional signatures in determining both movies’ and
users’ experiential qualities and assess how genre clusters differ in their eudaimonic and hedonic characteristics
based on these signatures. To the best of our knowledge, this is the first study to explore these connections.
Ultimately, our findings aim to enhance personalized recommender systems by aligning recommendations with
users’ emotional needs and desired experiences.
Keywords
Movie Recommender Systems, Emotional signature, Eudaimonia, Hedonia, Emotional experience
1. Introduction
Recommender systems in the movies domain aim to assist users with the movie selection decision-
making process [1]. The conventional approach involves predicting a calculated utility for a movie for
each user based on user-specific signals, either explicit (e.g., ratings) or implicit, behavior-based (e.g.,
play, stop, purchase) [1]. However, research has shown that the utility of the movie for a user is multi-
faceted [2], and that current algorithms limit the coverage of users’ tastes [3]. One of the fundamental
facets in movie consumption is the often overlooked emotional aspect [4]. The primary intention of
movies is to elicit emotions, generating an “ongoing, genuine emotional response“ [5], that audiences
are attracted to [6]. Bartsch [7] showed that users consume movies and TV shows for emotional
gratification. Yet, the sought after emotional experience is complex, as movies are made to elicit a range
of experiences, and also complex, as audiences seek various emotional experiences [8, 9, 5, 10] .
Emotions and the intended emotional experience play an important role in the decision-making
process [11, 12, 13, 14]. Lerner et al. [15] suggest, in their decision-making model, that the current
and expected emotions influence a decision. Emotions as factors in decision-making are specifically
important in the movie domain, as movies are made with the intent of eliciting emotions [16, 5]. The
choice of movie is based on the expected experience [17]. Furthermore, “effect elicitation, triggered
by emotions, was found to be a powerful reason for box office success” [18]. Additionally, Mokryn
et al. [19] showed that movies’ elicited emotional experience relates to success factors such as ratings
and box office earnings. Similarly, emotional regulation appears to be the main motive for consuming
music [20] and other media, such as video games and books [21].
Recently, positive psychology researchers argued that people expect both pleasure and meaning when
choosing a movie [8]. These expectations are described by the concepts of hedonia and eudaimonia [8],
IntRS’24: Joint Workshop on Interfaces and Human Decision Making for Recommender Systems, October 18, 2024, Bari (Italy)
Envelope-Open matej.golubovikj@famnit.upr.si (A. M. Golubovikj); omokryn@is.haifa.ac.il (O. Mokryn); marko.tkalcic@gmail.com
(M. Tkalčič)
GLOBE https://www.scan.haifa.ac.il/osnat-mokryn (O. Mokryn); https://markotkalcic.com (M. Tkalčič)
Orcid https://orcid.org/0009-0002-0378-400X (A. M. Golubovikj); https://orcid.org/0000-0002-1241-9015 (O. Mokryn);
https://orcid.org/0000-0002-0831-5512 (M. Tkalčič)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
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Workshop ISSN 1613-0073
Proceedings
which refer to two distinct experiences. A hedonic experience refers to movie consumption characterized
by pleasure and amusement. In contrast, a eudaimonic experience relates to life’s meaning and purpose [8].
For example, The Hangover (2009) is considered a movie that induces a hedonic experience, while
Manchester by the Sea (2016) induces a eudaimonic experience.
In this work, we explore the relationship between the emotions elicited by movies and their eu-
daimonic and hedonic characteristics. To identify a movie’s elicited emotions we build on the work
of Mokryn et al. [19], who showed that the emotions evoked in viewers when watching a movie can be
inferred from the movie’s reviews. They termed the inferred vector of basic emotions as the emotional
signature of a movie. Using movies’ emotional signatures and eudaimonic or hedonic characteristics, as
found by Tkalčič and Ferwerda [17], we address here the following three research questions (RQs):
• RQ1: What are the correlations between movie emotional signatures and eudaimonic and hedonic
characteristics?
RQ1.1: What are the correlations between movies’ emotional signatures and their respective
eudaimonic or hedonic characteristics?
RQ1.2: What are the correlations between the users’ eudaimonic or hedonic orientation
characteristics and the emotional signatures of the movies they liked?
• RQ2: Can the experience of a movie, either hedonic or eudaimonic, be predicted from its emotional
signature?
RQ2.1: Can we predict movies’ eudaimonic or hedonic characteristics from their emotional
signatures?
RQ2.2: Can we predict the users’ eudaimonic or hedonic orientation from the emotional
signatures of the movies they liked?
• RQ3: When movie genres are clustered based on the emotional signatures of the movies within
them, are there any statistical differences between these clusters in terms of their eudaimonic or
hedonic characteristics?
Our final goal is to use these findings to inform the design of better personalized recommender
systems. As people are choosing movies with the intent of receiving a specific meaningful emotional
experience [7, 5, 17], understanding the type of sought-after experience and the emotions associated
with it can help in the creation of personalized recommendations that can cater to the user’s various
moods. While this work focuses on the movie domain, similar studies can be conducted in other media
types, such as TV shows, music, video games, or books.
2. Related Work
We discuss here the importance of emotions, hedonia, and eudaimonia for decision-making supported
by recommender systems.
Emotions and the expected emotional experience are a fundamental part of choice-based decision-
making [11, 12, 13, 14]. In particular, the implied emotional effect of a decision is pivotal in the
construction of personal preferences [12, 13, 2].
Tan [5] describes movies as “emotion machines ... created with the intention of eliciting a wide range
of emotions”. Smith [16] describes a movie as an “invitation to feel”. Yet, it is impossible to infer from
the movie the emotions it will elicit in its audience, and the success of a movie lies in the gap between
the directors’ intent in this suggested invitation and the emotions experienced by the audience, which
can be assessed only after they have seen the movie [16, 22].
Mokryn et al. [19] showed rigorously that the emotions evoked by movies could be extracted from
movie reviews. When people write reviews for movies, they also share the emotions the movie elicited
in them [23]. To validate their hypothesis, [19] extracted values for each of Plutchik’s eight basic
emotions [24] from IMDb reviews, forming an ”emotional signature” for each movie as a vector of these
eight emotions. Their analysis confirmed that these emotional signatures align with the normative
emotional experiences elicited by the films, demonstrated through a series of experiments. They
established convergent validity by correlating the emotional signatures with manually measured evoked
emotions. Face validity was supported through various experiments, including visualizing the emotional
signatures and conducting genre analysis by calculating average emotional signatures for movies within
specific genres. Additionally, criterion-related validity was shown through experiments that revealed
a movie’s emotional signature could predict its genre and partially explain its success, as indicated
by ratings and box office revenue. They also observed that sequels tend to have emotional signatures
significantly more similar to each other than to randomly selected movies or movies within the same
genre.
In their seminal work, Oliver and Raney [8] explained the paradox that users often enjoy consuming
entertainment content that does not necessarily induce happiness. The inclination to be attracted to
other types of experiences, such as sadness, is explained by the fact that people have different needs.
One such need is the need to experience pleasure and positive aspects in general, referred to as hedonic
experience. The other is the need to engage in contemplation about truth, meaning, and purpose,
known as eudaimonic experience. Tkalčič and Ferwerda [17] demonstrated that in the movie domain,
there is a large variance in user preferences for eudaimonic or hedonic experiences, with some users
preferring one over the other, while many prefer to consume both, at different times.
In general, the quality of the experience, whether eudaimonic or hedonic, can be broken down into
two parts: the item and the user counterpart. In our case, the item is a movie. To have, for example,
a eudaimonic experience, (i) the user needs to be inclined to have it, and (ii) the movie should have
the potential (or being perceived as having the potential) to elicit it. We refer to the former as the
eudaimonic (or hedonic) orientation of the user and to the latter as the eudaimonic (or hedonic) perception
of the movie. Further research on eudaimonia and hedonia in relation to recommender systems has
shown that item perception can be predicted from various signals, such as song lyrics [25], movie
subtitles [26] or movies’ audio and visual features [27]. The user orientation part can be predicted from
user interactions with recommender systems [28, 29].
Both emotions and eudaimonia/hedonia have been shown to contribute to the performance of
recommender systems. Zheng et al. [30] has successfully used emotions as contextual variables to
improve the accuracy of recommender systems. In a recommender system for images, the target
emotion proved to be the most important predictor of image ratings [31]. Motamedi et al. [32] have
shown in a real-world application that the relevance of music videos can be predicted from eudaimonic
and hedonic qualities.
Although both emotions and eudaimonia/hedonia have received considerable attention in research,
no work so far has attempted to connect these two constructs. In this work we aim at filling this gap in
knowledge by showing how these two constructs are related.
3. Experiments
Our goal is to identify potential relationships between movies’ elicited emotions and their hedonic and
eudaimonic characteristics at both the movie and genre levels, as well as the user’s identified hedonic
and eudaimonic orientation. In our experiments, we define the following variables:
• Movie’s emotional signature: a vector describing the emotions a movie elicits.
• Genre’s emotional signature: a vector describing the expected emotions when watching films in
that genre.
• Movie’s eudaimonic and hedonic perception (EP, HP). Two numerical values representing how
users perceive the potential of a movie to generate a eudaimonic or hedonic experience.
• User’s eudaimonic and hedonic orientation (EO, HO). Two numerical values describing the inclina-
tion of the user to seek eudaimonic or hedonic experiences.
To address the three research questions we ran three studies: (i) a correlational analysis to establish
relationships between the observed variables, (ii) predictive models trained to predict movies’ EP and
HP, and users’ EH, and HO from emotional signatures, and (iii) a genre-based comparison of the genres’
movies’ mean EP and EP.
3.1. Datasets
We merged two datasets, the emotional signatures dataset [19] and the eudaimonia/hedonia (E/H)
dataset [29]. For genre clustering, we additionally used the GenreData dataset as explained below.
The data acquisitions for (i) the emotional signatures dataset and (ii) the eudaimonia/hedonia (E/H)
dataset, were carried out in their respective works, (i) Mokryn et al. [19] and (ii) Motamedi et al. [29].
For completeness, we will summarize the data acquisition of these two datasets below.
For the emotional signatures dataset, we collected reviews and additional movie data for 20,514
movies from the IMDb database spanning 2021 and 2022. The emotional signatures were generated
according to the method described in [33, 19, 10]. The final dataset includes the following details for
each movie: name and ID, director(s), cast, genres, rating, synopsis, plot, release year, and the emotional
signature extracted from the reviews [19].
Specifically, the emotional signatures were derived as follows. We aggregated the reviews for
each movie into a single document and created a Bag of Words (BoW) per document. This involved
tokenizing, normalizing, and stemming the text, while omitting stop words. Using the NRC lexicon [34],
we annotated the words in the BoW for each of the Plutchik’s eight basic emotions. Then, for each movie,
the frequency of each emotion was calculated based on occurrences in the annotations. Following a
normalization phase, the resulting set of emotion values forms the vector termed the emotional signature
of a movie. This is an eight-dimensional vector, with each dimension representing the strength of the
corresponding basic emotion, namely anger, anticipation, disgust, fear, joy, sadness, surprise, and trust.
The additional dataset used for genre calcualtions, GenreData, includes 2937 movies that were
assigned various genre labels by their distributors. There were 21 genres in total, and most movies
(89.4%) had more than one genre label. The majority of movies were tagged as belonging to 2, 3, or 4
genres (27.4%, 33.2%, 19.8%, respectively).
The E/H dataset was collected through a user study. A total of 350 participants were asked to
annotate1 how they perceived the eudaimonic and hedonic quality of several movies. On average each
movie was annotated by five users. These multiple annotations were than aggregated into the mean
movie eudaimonic perception (EP) and the mean movie hedonic perception (HP). Additionally, the
participants in the user study provided ratings for the movies. On average, each user provided ratings
for 10 movies. Each participant also filled in a questionnaire on their inclinations to prefer hedonic
and eudaimonic content. This yielded, for each user, their user eudaimonic orientation (EO) and user
hedonic orientation (HO) scores.
We merged the two datasets by the title and year fields. In total we had 410 movies with both
emotional signatures and E/H annotations. Table 2 shows an excerpt from the merged dataset while
the descriptive statistics are provided in Tab. 1.
Ratings Users Movies
Merged Dataset 2408 350 410
Dataset used for user profile analysis 1562 240 370
Dataset used for movie profile analysis 1946 345 225
Table 1
Dataset Summary
3.2. Correlational Analysis
We analyzed the correlations between emotional signatures and eudaimonic and hedonic variables from
two perspectives: (i) movie-centric and (ii) user-centric.
In the first correlational analysis we compute correlations between the basic emotions elicited by
a movie according to its emotional signature and its average eudaimonic and hedonic perceptions.
1
The annotations were in the range from 1 to 7 and derived using the questionnaire detailed in Motamedi et al. [29].
Anticipation
Movie ID
Sadness
Surprise
User ID
Disgust
Rating
Anger
Trust
Fear
HO
HP
EO
EP
Joy
63 tt043440 0.09 0.11 0.05 0.13 0.09 0.06 0.06 0.18 4.7 6.0 4.0 4.0 3
313 tt185372 0.10 0.13 0.06 0.12 0.11 0.08 0.06 0.13 5.3 6.7 4.5 5.5 5
Table 2
Excerpt of the dataset used. Each data point contains the userID, movieID, a set of emotions from the emotional
signature of that movie, the movie’s eudaimonic perception and hedonic perception, the user’s eudaimonic
orientation and hedonic orientation, and finally the rating the user has given to this movie.
To make sure that we have enough variability when calculating the average EP and HP of movies,
we included only movies with at least 5 scores for eudaimonic and hedonic perceptions from users.
We had a total of 225 such movies (ref. Table 1), which we used in this analysis. Table 3 shows the
results of the analysis. The movies’ eudaimonic and hedonic perceptions are strongly correlated with
several basic emotions. Specifically, we find that movies with high hedonic perception tend to have
above-average positive emotions, i.e., high anticipation, and joy. They are also correlated with low
values in some of the negative emotions, i.e., low fear and sadness. Surprisingly, they also correlate
with high surprise. Movies with high eudaimonic perception, on the other hand, seem to not elicit
strong emotional responses, and have average or below average values for the emotions anticipation,
disgust, joy, and surprise.
Movie Movie
Eudaimonic Hedonic
Perception Perception
(EP) (HP)
EP 1.0*** -0.4***
HP -0.4*** 1.0***
Anger -0.09 -0.08
Anticipation -0.15* 0.31***
Disgust -0.19** -0.04
Fear 0.02 -0.21**
Joy -0.18** 0.41***
Sadness 0.05 -0.22**
Surprise -0.27*** 0.31***
Trust 0.07 0.02
Table 3
Correlations between movies’ elicited basic emotions as identified in their emotional signatures and movies’
eudaimonic and hedonic characteristics.
In the second analysis we consider users. We examine the correlation between a user’s eudaimonic
and hedonic orientation and emotional signatures of the movies the user liked, i.e., those they rated
with 4 or 5 stars. To ensure that we had enough variability in emotional signatures per user, we included
only users who rated at least five movies with 4 or 5 stars. We had a total of 240 such users (ref. Table
1), which we used for this analysis.
The correlation results, shown in Tab. 4, are weak. Generally, users with a high eudaimonic orientation
prefer movies with a wide range of anger, as indicated by the high standard deviation of anger in their
liked movies. The movies they prefer tend to have lower levels of anticipation, and joy, and higher
levels of sadness. In contrast, movies liked by users with a high hedonic orientation typically display
average levels of all emotions, except for high anticipation and a significant variance in anger.
User User User User
Eudaimonic Hedonic Eudaimonic Hedonic
Orientation Orientation Orientation Orientation
(EO) (HO) (EO) (HO)
EO 1.0*** -0,04 HO -0,04 1.0***
Anger (mean) 0.13* -0,1 Anger (stdev) 0.17** -0.13*
Anticipation (mean) -0.14* 0.16* Anticipation (stdev) 0,01 0,09
Disgust (mean) 0,1 0 Disgust (stdev) 0,09 0,06
Fear (mean) 0,1 0,01 Fear (stdev) 0,11 -0,07
Joy (mean) -0.19** 0,05 Joy (stdev) -0,08 0
Sadness (mean) 0.23*** -0,1 Sadness (stdev) 0.2** -0,11
Surprise (mean) -0,03 0,02 Surprise (stdev) 0,1 -0,08
Trust (mean) -0,1 -0,04 Trust (stdev) -0,06 -0,05
Table 4
Correlation between the user eudaimonic and hedonic orientations, and the emotional signatures of the movies
the user liked, i.e. rated as 4 or 5.
3.3. Predictions
Here we also look at our problem from two perspectives, movies and users. We aim at answering:
• Can we predict movies’ eudaimonic perception and hedonic perception from their emotional
signatures?
• Can we predict the users’ eudaimonic orientation and hedonic orientation from the emotional
signatures of the movies they liked?
To predict the movie EP and HP we used the movie emotions in the emotional signatures as features.
We evaluated only two simple models – linear regression and random forest – to gain an initial
understanding of the predictive potential of these features, as this is early work. The baseline method
was the mean predictor of the target variable. We tuned the hyperparameters of the models utilizing
grid search, and evaluated the models through five-fold cross-validation. RMSE and MAE were chosen
as metrics because the target variables were ordinal continuous.
In the second prediction task, predicting users’ eudaimonic and hedonic orientation, we used as
features the emotions from the emotional signatures of movies the users liked by filtering only movies
with a rating of 4 or 5. The other details (models, hyperparameter tuning and splitting) are the same as
for the first prediction task.
The results in Tab. 5 show that, except for the prediction of the user hedonic orientation, all predictors
beat the mean baseline. In the case of the prediction of movies’ eudaimonic perception the difference
in RMSE is statistically significant at 𝑝 < 0.05 using a paired t-test. This calls for further prediction
experimentation with larger datasets and more complex and optimized models. In summary, the
emotional signatures of movies can predict their eudaimonic and hedonic perception, and the emotional
signatures of movies rated highly by users can predict the users’ orientation, either eudaimonic or
hedonic.
3.4. Between-cluster Comparison
Here, we wanted to investigate the relationship between movies’ emotional signatures, their eudaimonic
and hedonic experiences, and movie genres. Mokryn et al. [33] explored the emotional signatures of
genres and showed that movies’ emotional signatures can predict the genre of a movie, when the movie
has one genre label. Here, we continue to see whether genres that have “close” emotional signatures
also have similar quality, either hedonic or eudaimonic. To that end, we cluster the genres based on
their emotional signatures and observe the differences in the values of the eudaimonic perception and
hedonic perception between clusters of movies.
Prediction Method RMSE MAE 𝑅2
Mean Baseline 1.0266 0.8443 -0.0172
Movies’ EP Linear Regression 0.9852 0.8102 0.0531
Random Forest 0.9603* 0.7863 0.1094
Mean Baseline 0.6645 0.5480 0.0418
Movies’ HP Linear Regression 0.5790 0.4542 0.1992
Random Forest 0.5799 0.4602 0.2005
Mean Baseline 1.0635 1.0635 -0.0537
Users’ EO Linear Regression 1.0984 0.8719 -0.1283
Random Forest 1.0549 0.8324 -0.0384
Mean Baseline 1.2730 1.0135 -0.0929
Users’ HO Linear Regression 1.3838 1.0736 -0.2882
Random Forest 1.2910 1.0283 -0.1208
Table 5
Results from our predictive modelling of movies eudaimonic and hedonic perceptions (EP, HP), as well as users’s
eudaimonic and hedonic orientations (EO, HO). The best-performing models are marked in bold. Statistically
significant results are marked with *. All prediction targets (EP, HP, EO, HO) are in the range from 1 to 7.
To cluster genres together according to their emotional signatures we used the GenreData dataset
(described in Section 3.1) and performed k-means clustering on the emotional signatures of genres.
Similar to the emotional signatures of movies (as described for movies in Section 3), and following the
process in [19], we calculated a genre’s emotional signature by aggregating the reviews of all movies
belonging to a specific genre into a single document and computing the emotional signature of that
document.
The k-means clustering was performed with 𝑘 = 3 clusters2 . All genres but one (horror) were divided
into the clusters Comedy, Action. At this stage we assigned movies to one of the clusters according
to their genres. As most movies (89%) have multiple genre labels, we mapped a movie to the cluster
that contained most of its genres. For example, a movie with the genres Comedy (Comedy), Romance
(Comedy), Family (Comedy), and Mystery (Action), was placed in Comedy. Horror movies were placed
only in the Horror cluster. We excluded movies for which the genre tie could not be broken, e.g., a
movie with the genres Comedy (Comedy), Romance (Comedy), Action (Action), and Mystery (Action).
We then calculated for each cluster of Comedy, Action and Horror its emotional signature and its cluster
HP and EP values. Given the ordinal nature of the the within-cluster movies EP and HP variables, we
used the Mann-Whitney U test for testing the difference of means of EP and HP between the clusters.
The results, summarized in Tab. 6, show that the differences between clusters in terms of eudaimonic
perception and hedonic perception are almost always statistically significant. The average within-cluster
movie EP is the highest in the action cluster and lowest in the horror cluster, which is to be expected.
Although the comedy cluster has a lower EP than the action cluster, the difference is not significant.
With regards to the movies HP, the comedy cluster has it higher and statistically significant than the
other two clusters, whose values are almost the same.
4. Discussion
Getting back to our research questions, we have shown that emotional signatures, movies’ eudaimonic
perception and hedonic perception, and users’ eudaimonic orientation and hedonic orientation are
correlated. This is important when we think that all these constructs are well-supported in psychology
2
We tested different values for 𝑘, in the range of 2 to 15, and chose 𝑘 = 3 using the elbow method.
Avg. EP within Avg. HP within
Cluster Genres U-Test
cluster cluster
Diff. C1 and C2:
Comedy, Romance, Family,
C1: Comedy 3.680 5.139 EP p<0.001;
Animation, Musical, Sport, Music
HP p<0.001;
Crime, Sci-Fi, Adventure, Diff. C1 and C3:
C2: Action Fantasy, Western, Mystery, 4.297 4.601 EP p>0.5;
War, Documentary, Biography, HP p<0.01;
Thriller, Action, Drama, History
Diff. C2 and C3:
C3: Horror Horror 3.432 4.473 EP p<0.01;
HP p>0.05;
Table 6
Summary of cluster analysis. The average movie eudaimonic perception (EP) and hedonic perception (HP) of
each of the three clusters are reported. In the last column are the p–values of the Mann-Whitney U test of the
current cluster compared to the other two clusters.
research, valid, and have numerous measurement instruments.
Not only have we shown that there are correlations between the constructs, we also demonstrated
that this correlation can be used to perform predictions from one set of constructs to the other.
Finally, we have also shown that movie genres, which stem from the movie making process, have
substantially different emotional signatures and eudaimonic/hedonic characteristics.
4.1. Where do we go from here?
There is always room for expanding our understanding of the topic by running larger studies, collecting
more data and using more complex models.
However, the potential, we believe, lies in the knowledge bound to these constructs that stems mostly
from psychology but also from other domains, such as movie making. For example, screen-writers and
directors aim at eliciting certain emotions in viewers.
The most important challenge in the future is how to take advantage of this knowledge to improve
recommender systems. There are several ways one could go about it. The most obvious is using
these features to improve rating predictions. Another option is to use these constructs to cluster the
movies and adjust the ranking according to the clusters. As these constructs describe the elicited
experience, which is a sought experience, a connection with intent-based recommendations is a viable
way of investigation. One could also investigate how context influences the users’ eudaimonic and
hedonic orientations. For example, a user may be generally inclined to have eudaimonic experiences,
but if she’s really tired in the evening, she might prefer to pursue a hedonic experience to chill out.
Hence, an interesting question would also to tie these orientations to user’s moods, and how to match a
recommendation to changing users’ moods.
Additionally, we see explainable recommender systems having a strong potential with the constructs
of emotions and eudaimonia/hedonia experiences. Recommender systems algorithms, especially those
based on latent features, are hard to explain. However, the constructs we propose are not just inter-
pretable features but have also a strong background knowledge in psychology that can be leveraged for
generating explanations. As Miller [35] stated in his seminal work, “explanations are social — they are a
transfer of knowledge, presented as part of a conversation or interaction, and are thus presented relative to
the explainer’s beliefs about the explainee’s beliefs.” The general familiarity of these constructs and the
possibility of increasing this familiarity with knowledge from psychology opens new possibilities for
explainable recommender system.
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