# Prediction of Eudaimonic and Hedonic Orientation of Movie Watchers
## 1 Introduction  
Personality traits have been defined as the _most important ways in which individuals differ in their enduring emotional, interpersonal, experiential, attitudinal and motivational styles_[1]. These factors have shown their utility in many online applications, such as recommender systems [2] and targeted advertising [3]. However, the usage of lengthy questionnaires, which is the typical method of assessing a user's personality, is not practical for online applications because they are intrusive and time-consuming. For this reason, personality prediction from the digital traces of users emerged about a decade ago using mostly social media sources to infer users' personality traits [4; 5; 6; 7]. As of today, advanced algorithms [8] and even off-the-shelf tools ([[https://applymagicsauce.com/demo](https://applymagicsauce.com/demo)]([https://applymagicsauce.com/demo](https://applymagicsauce.com/demo)) (accessed on 15 September 2022), [[https://www.ibm.com/no-en/cloud/watson-personality-insights](https://www.ibm.com/no-en/cloud/watson-personality-insights)]([https://www.ibm.com/no-en/cloud/watson-personality-insights](https://www.ibm.com/no-en/cloud/watson-personality-insights)) (accessed on 15 September 2022)) exist for personality prediction.  
The majority of personality prediction approaches focuses on the prediction of personality in the form of the Five Factor Model (FFM), with the five factors being Openness, Conscientiousness, Extraversion, Agreeableness and Neuroticism [9]. This is a generic personality model that covers all areas of human activity. For more specific domains, other personality models have been devised, such as the [PERSON] model for gaming [10], the vocational RIASEC model [11], and the conflict-coping Thomas-Kilman model [12].  
In this paper, we focus on a personality model that describes the motivations for multimedia consumption. The model is composed of two factors--namely, the _eudaimonic_ and _hedonic orientation of users_. The first factor, the eudaimonic orientation of users, accounts for how much a user is looking for meaning in multimedia content, while the the secondfactor, hedonic orientation, accounts for how much a user is looking for entertaining quality [13]. This model has potential for user modeling and recommender systems in the entertainment domain, as demonstrated in [14].  
In particular, in scenarios, when items are described with their eudaimonic and hedonic qualities (e.g., the movie _Manchester by the Sea_ has high eudaimonic but low hedonic quality), a content-based filtering approach can be useful for recommendations [15]. Thus far, there has been no work on predicting the eudaimonic and hedonic orientation (EHO) personality model of users from digital traces.  
In this paper, we propose a model that takes features generated from traces of interactions with movies to predict EHO. We demonstrate the quality of the predictions on a dataset of 350 users, 703 movies and 3499 ratings. The results of our analysis are examined based on the information users provide, the interactions they have with the system, or both. According to our analysis, our proposed approach predicts the EO and HO of users substantially better than baseline approaches.