Titulo Estágio
Enhancing Mental Health Status with an Artistic Content Recommender System.
Áreas de especialidade
Sistemas Inteligentes
Sistemas Inteligentes
Local do Estágio
Flainar and Centre for Informatics and Systems of the University of Coimbra (CISUC), at the Department of Informatics Engineering of the University of Coimbra, and at the Institute for Chemical and Bioengineering, at the Department of Chemistry and Applie
Enquadramento
The problem of mental health is a pressing concern in today's society, with millions of people worldwide experiencing conditions such as depression, anxiety, and stress. These mental health challenges can significantly impact an individual's well-being, relationships, and overall quality of life. In the context of mental health treatment, art, including movies, books, music, and other forms of creative expression, has been recognized for its therapeutic potential. Art has the power to evoke emotions, provide an outlet for self-expression, and offer a sense of connection and meaning.
In today's digital age, Artificial Intelligence (AI) has emerged as a transformative technology with wide-ranging applications. Its relevance and impact span across various industries, revolutionizing the way we live, work, and interact. AI systems possess the ability to analyze massive amounts of data, recognize patterns, and make intelligent decisions, enabling advancements in areas such as healthcare, finance, transportation, and more. One significant area where AI has made significant strides is in recommender systems. Recommender systems leverage AI algorithms to provide personalized recommendations to users, helping them navigate through vast amounts of information and discover relevant content, products, or services. Whether it's suggesting movies based on viewing history, recommending products based on purchase behavior, or offering personalized news articles based on interests, recommender systems have become indispensable in enhancing user experiences, increasing customer engagement, and driving business growth. The continuous advancements in AI and recommender systems hold immense potential to further revolutionize how we discover, consume, and interact with information and make informed choices in our daily lives.
Providing personalized artistic content through a recommender system can be a valuable solution in promoting mental well-being. A recommender system that understands an individual's preferences, needs, interests, and emotional state can curate a tailored selection of movies, books, music, and other artistic content that align with their specific needs and mental state. By offering personalized recommendations, the system can effectively address the unique challenges and preferences of each individual, maximizing the potential therapeutic benefits of art.
Leveraging a recommender system to provide personalized artistic content holds great promise in the realm of mental well-being and health. By acknowledging the therapeutic potential of art and tailoring recommendations to individuals' unique needs, interests, and emotional states, we can harness the power of technology to enhance mental well-being, provide support in the treatment of mental health conditions, and ultimately improve the quality of life for individuals experiencing these challenges.
At Flainar, we draw on behavioral, cognitive and constructivist psychological models. The intervention focuses on the cultural prescription of contents and activities. Our content library includes text and audio of literary excerpts and historical narratives, biographies of Portuguese writers, illustrations and animated short films. Prescription can include walks combined with georeferenced content and museum visits.
Objetivo
The aim of this work is to develop a recommender system for providing artistic content to individuals according to their unique needs, interests, and emotional states in order to enhance their mental well-being. Based on this research aim, the following research objectives can be identified:
1. Develop an effective recommendation algorithm: Design and implement an advanced recommendation algorithm that leverages user data to generate personalized artistic content recommendations. This involves exploring various recommendation techniques, such as collaborative filtering, content-based filtering, or hybrid approaches, and adapting them to the specific context of artistic content recommendations.
2. Incorporate user feedback and iterative improvement: Implement mechanisms to gather user feedback on recommended artistic content and evaluate its impact on their mental well-being. Continuously iterate and improve the recommender system based on user feedback, incorporating mechanisms for user ratings, reviews, and adaptive learning algorithms to enhance recommendation accuracy over time.
3. Ethical considerations and user privacy: Address ethical concerns and privacy issues associated with collecting and utilizing user data. Develop appropriate safeguards, consent mechanisms, and data anonymization techniques to protect user privacy and ensure compliance with relevant regulations and guidelines.
4. Deployment and usability testing: Deploy the recommender system in a real-world setting and conduct usability testing to evaluate its performance, reliability, and user experience. Gather feedback from users, system administrators, or mental health professionals to refine the system's usability, address any technical issues, and ensure seamless integration into users' daily lives.
By accomplishing these research objectives, the development of a recommender system for providing personalized artistic content to enhance individuals' mental well-being can be effectively realized, contributing to the advancement of technology-assisted mental health support and promoting overall mental wellness.
Plano de Trabalhos - Semestre 1
1- State of the art [Sept – Oct]
2- Problem statement, research aims and objectives [Nov]
3- Design and first implementation of the recommender system [Nov – Jan]
4- Thesis proposal writing [Dec – Jan]
Plano de Trabalhos - Semestre 2
5- Improvement of the recommender system [Feb – Apr]
6- Experimental Tests [Apr – May]
7- Paper writing [May – Jun]
8- Thesis writing [Jan – Jul]
Condições
The work should take place both at Flainar (remote or at IPN Coimbra) and at the Centre for Informatics and Systems of the University of Coimbra (CISUC), at the Department of Informatics Engineering of the University of Coimbra.
A 541,12 euros per month salary is foreseen for the 2nd Semester (6 months) plus a prize of 1200 euros, depending on the performance of the student.
The candidate must have a very good background knowledge in Artificial Intelligence, especially in the areas of Artificial Intelligence that the internship falls within.
Observações
Advisors:
Luís Macedo, Vasco Pereira, Carlos Figueiredo (Flainar).
Orientador
Luís Macedo
macedo@dei.uc.pt 📩