Loading...
4 results
Search Results
Now showing 1 - 4 of 4
- A Multiple Criteria Decision Analysis Framework for Dispersed Group Decision-Making ContextsPublication . Carneiro, João; Martinho, Diogo; Alves, Patrícia; Conceição, Luís; Marreiros, Goreti; Novais, PauloTo support Group Decision-Making processes when participants are dispersed is a complex task. The biggest challenges are related to communication limitations that impede decision-makers to take advantage of the benefits associated with face-to-face Group Decision-Making processes. Several approaches that intend to aid dispersed groups attaining decisions have been applied to Group Decision Support Systems. However, strategies to support decision-makers in reasoning, understanding the reasons behind the different recommendations, and promoting the decision quality are very limited. In this work, we propose a Multiple Criteria Decision Analysis Framework that intends to overcome those limitations through a set of functionalities that can be used to support decision-makers attaining more informed, consistent, and satisfactory decisions. These functionalities are exposed through a microservice, which is part of a Consensus-Based Group Decision Support System and is used by autonomous software agents to support decision-makers according to their specific needs/interests. We concluded that the proposed framework greatly facilitates the definition of important procedures, allowing decision-makers to take advantage of deciding as a group and to understand the reasons behind the different recommendations and proposals.
- Modeling Tourists' Personality in Recommender SystemsPublication . Alves, Patrícia; Saraiva, Pedro; Carneiro, João; Campos, Pedro; Martins, Helena; Novais, Paulo; Marreiros, GoretiPersonalization 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.
- Anomaly Detection on Natural Language Processing to Improve Predictions on Tourist PreferencesPublication . Meira, Jorge; Carneiro, João; Bolón-Canedo, Verónica; Alonso-Betanzos, Amparo; Novais, Paulo; Marreiros, GoretiArgumentation-based dialogue models have shown to be appropriate for decision contexts in which it is intended to overcome the lack of interaction between decision-makers, either because they are dispersed, they are too many, or they are simply not even known. However, to support decision processes with argumentation-based dialogue models, it is necessary to have knowledge of certain aspects that are specific to each decision-maker, such as preferences, interests, and limitations, among others. Failure to obtain this knowledge could ruin the model’s success. In this work, we sought to facilitate the information acquisition process by studying strategies to automatically predict the tourists’ preferences (ratings) in relation to points of interest based on their reviews. We explored different Machine Learning methods to predict users’ ratings. We used Natural Language Processing strategies to predict whether a review is positive or negative and the rating assigned by users on a scale of 1 to 5. We then applied supervised methods such as Logistic Regression, Random Forest, Decision Trees, K-Nearest Neighbors, and Recurrent Neural Networks to determine whether a tourist likes/dislikes a given point of interest. We also used a distinctive approach in this field through unsupervised techniques for anomaly detection problems. The goal was to improve the supervised model in identifying only those tourists who truly like or dislike a particular point of interest, in which the main objective is not to identify everyone, but fundamentally not to fail those who are identified in those conditions. The experiments carried out showed that the developed models could predict with high accuracy whether a review is positive or negative but have some difficulty in accurately predicting the rating assigned by users. Unsupervised method Local Outlier Factor improved the results, reducing Logistic Regression false positives with an associated cost of increasing false negatives.
- An Intelligent Coaching Prototype for Elderly CarePublication . Martinho, Diogo; Crista, Vítor; Carneiro, João; Corchado, Juan Manuel; Marreiros, GoretiThe world ageing problem is prompting new sustainable ways to support elderly people. As such, it is important to promote personalized and intelligent ways to assure the active and healthy ageing of the population. Technological breakthroughs have led to the development of personalized healthcare systems, capable of monitoring and providing feedback on different aspects that can improve the health of the elderly person. Furthermore, defining motivational strategies to persuade the elderly person to be healthier and stay connected to such systems is also fundamental. In this work, a coaching system is presented, especially designed to support elderly people and motivate them to pursue healthier ways of living. To do this, a coaching application is developed using both a cognitive virtual assistant to directly interact with the elderly person and provide feedback on his/her current health condition, and several gamification techniques to motivate the elderly person to stay engaged with the application. Additionally, a set of simulations were performed to validate the proposed system in terms of the support and feedback provided to the user according to his progress, and through interactions with the cognitive assistant.