The use of personality to predict user preferences or select a job candidate are practical examples of how personality can be used in a variety of fields. But to assess someone's personality is not easy. The most commonly used techniques are personality questionnaires, but they are subject to social desirability bias and false responses, limiting the accuracy of personality assessment. Implicit techniques have also been used, but they require great amounts of user interactions with the system, and most of them only focus on broader personality dimensions. To overcome those limitations, we developed a short-duration mobile serious game, Coin Catcher, as a concept proof to implicitly measure the granular personality trait morality in less than 5 min. The game uses concepts related to normative morality, proposing dilemmas that the player must solve in order to progress in the game. Experiments with real users were conducted ($n=96$), showing morality can be implicitly determined by a simple game in a short period of time, without the bias associated with personality questionnaires. Additionally, correlations with other personality traits, such as altruism, cooperation, modesty, sympathy, and anger were also found, showing the game has the potential to measure several granular personality traits that better characterize a person.

Personality plays a crucial role in predicting preferences, behaviors, and interactions. Its importance in accurately characterizing individuals has led to its application in areas such as movies, music, and tourism. Although personality questionnaires have traditionally been used to measure personality, they are prone to biases, such as inflated or false responses. In response to these limitations, serious games emerge as innovative alternatives for assessing personality, studying the player's behavior. This study developed and evaluated Mindful Escape, a short duration mobile serious game, as a concept proof to implicitly measure the personality trait of cooperation. The game adapts concepts from the Prisoner's Dilemma and the Tragedy of the Commons to create an Escape Room environment to encourage both cooperative and competitive interactions. Experiments with real users were performed (\(n\)=78), where significant correlations between the game's metrics and cooperation were identified. Additionally, other traits such as modesty, morality, altruism, and anger, also showed correlations. The game's duration exceeded the planned 5min, averaging ca. 10min, mainly due to difficulties related to gameplay by less experienced users, which needs to be addressed in the future. Nevertheless, the participants’ feedback was highly positive, highlighting the immersive and engaging experience offered by the game. The results show short-duration mobile games offer a viable and unobtrusive method for assessing users' detailed personality traits, paving the way to replace traditional personality questionnaires, and their integration into personality-based systems.

The complexity associated with groups of tourists led to the emergence of Group Recommender Systems (GRS) for tourism. But if generating recommendations for small groups is a complex task, to provide them to large and occasional groups is even more. This complexity is especially due to the group’s heterogeinity, conflicting preferences, the information overload found on the internet and the tourists’ different ways of coping with the information, hindering the recommendation process from the users’ profile construction to the final recommendation of a list of points of interest to visit. In this work, we show how we tackled the identified issues in a GRS prototype, Grouplanner, including the cold-start problem, by predicting the tourists’ preferences based only on their personality and dividing the main group into subgroups of similar personality; by using a Multi-Agent Microservice; a novel dynamic clustering algorithm, d-means, adapted from the k-means algorithm, that does not need to know the number of clusters a priori; and association rules. Using a personality dataset of n=100k users, the proposed d-means algorithm was tested against two baselines (k-means and k-means++), showing better results in the clustering quality and scalability. We were also able to determine a large set of association rules to refine the recommendations, although further improvements are needed. To test the Grouplanner prototype, a simulation with real users (n=35) was conducted. The results showed the subgroups formed were very compact, revealing a very good clustering quality, with an average silhouette of s = 0.91. 11 of the 15 proposed tourist preferences were successfully predicted and used for the preliminary recommendation lists, being 92 % of the participants satisfied with the individual recommendations and 96 % with the group recommendations.

The complexity associated to group recommendations needs strategies to mitigate several problems, such as the group's heterogeinity and conflicting preferences, the emotional contagion phenomenon, the cold-start problem, and the group members’ needs and concerns while providing recommendations that satisfy all members at once. In this demonstration, we show how we implemented a Multi-Agent Microservice to model the tourists in a mobile Group Recommender System for Tourism prototype and a novel dynamic clustering process to help minimize the group's heterogeneity and conflicting preferences. To help solve the cold-start problem, the preliminary tourist attractions preference and travel-related preferences & concerns are predicted using the tourists' personality, considering the tourists’ disabilities and fears/phobias. Although there is no need for data from previous interactions to build the tourists’ profile since we predict the tourists’ preferences, the tourist agents learn with each other by using association rules to find patterns in the tourists' profile and in the ratings given to Points of Interest to refine the recommendations.

Abstract To travel in leisure is an emotional experience, and therefore, the more the information about the tourist is known, the more the personalized recommendations of places and attractions can be made. But if to provide recommendations to a tourist is complex, to provide them to a group is even more. The emergence of personality computing and personality-aware recommender systems (RS) brought a new solution for the cold-start problem inherent to the conventional RS and can be the leverage needed to solve conflicting preferences in heterogenous groups and to make more precise and personalized recommendations to tourists, as it has been evidenced that personality is strongly related to preferences in many domains, including tourism. Although many studies on psychology of tourism can be found, not many predict the tourists’ preferences based on the Big Five personality dimensions. This work aims to find how personality relates to the choice of a wide range of tourist attractions, traveling motivations, and travel-related preferences and concerns, hoping to provide a solid base for researchers in the tourism RS area to automatically model tourists in the system without the need for tedious configurations, and solve the cold-start problem and conflicting preferences. By performing Exploratory and Confirmatory Factor Analysis on the data gathered from an online questionnaire, sent to Portuguese individuals from different areas of formation and age groups ( n = 1035), we show all five personality dimensions can help predict the choice of tourist attractions and travel-related preferences and concerns, and that only neuroticism and openness predict traveling motivations.

Personalization is increasingly being perceived as an important factor for the effectiveness of Recommender Systems (RS). This is especially true in the tourism domain, where travelling comprises emotionally charged experiences, and therefore, the more about the tourist is known, better recommendations can be made. The inclusion of psychological aspects to generate recommendations, such as personality, is a growing trend in RS and they are being studied to provide more personalized approaches. However, although many studies on the psychology of tourism exist, studies on the prediction of tourist preferences based on their personality are limited. Therefore, we undertook a large-scale study in order to determine how the Big Five personality dimensions influence tourists' preferences for tourist attractions, gathering data from an online questionnaire, sent to Portuguese individuals from the academic sector and their respective relatives/friends (n=508). Using Exploratory and Confirmatory Factor Analysis, we extracted 11 main categories of tourist attractions and analyzed which personality dimensions were predictors (or not) of preferences for those tourist attractions. As a result, we propose the first model that relates the five personality dimensions with preferences for tourist attractions, which intends to offer a base for researchers of RS for tourism to automatically model tourist preferences based on their personality.

The incorporation of Augmented Reality (AR) in the windshield of automobiles using heads-up displays (HUD) is starting to be implemented by some manufacturers and is proving to be very useful in some situations, including safety distance keeping. This paper reports on a system that warns the driver via the car's HUD when he violates the predefined safety distance, to avoid a possible forward collision, and proposes two different visualization metaphors based on traffic signals. The metaphors are compared, with and without warning sounds, through computer simulations performed by 22 participants from different age groups and driving experiences. One of the metaphors corresponds to a variant of the traffic sign C10 that forbids circulating from the preceding vehicle shorter than a certain minimum headway, whereas the other corresponds to the road safety marks, which recommend the safety distance to be observed from the vehicle ahead. Results show that the metaphor derived from the safety marks, with warning sounds, is preferred by the participants, and was considered the most useful, intuitive and adequate to a forward collision warning. We also found that this metaphor was preferred by all participants that were 42 or more years old, whereas participants between 28 and 41 years old were divided between the two metaphors, with warning sounds.