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- Context-based multi-agent recommender system, supported on IoT, for guiding the occupants of a building in case of a firePublication . Neto, Joaquim; Morais, A. Jorge; Gonçalves, Ramiro Manuel Ramos Moreira; Coelho, António LeçaThe evacuation of buildings in case of fire is a sensitive issue for civil society that also motivates the academic community to develop and study solutions to improve the efficiency of evacuating these spaces. The study of human behavior in fire emergencies has been one of the areas that have deserved the attention of researchers. However, this modeling of human behavior is difficult and complex because it depends on factors that are difficult to know and that vary from country to country. In this paper, a paradigm shift is proposed which, instead of focusing on modeling the behavior of occupants, focuses on conditioning this behavior by providing real-time information on the most efficient evacuation routes. Making this information available to occupants is possible with a solution that takes advantage of the growing use of the IoT (Internet of Things) in buildings to help occupants adapt to the environment. Supported by the IoT, multi-agent recommender systems can help users to adapt to the environment and provide the occupants with the most efficient evacuation routes. This paradigm shift is achieved through a context-based multi-agent recommender system based on contextual data obtained from IoT devices, which recommends the most efficient evacuation routes at any given time. The obtained results suggest that the proposed solution can improve the efficiency of evacuating buildings in the event of a fire; for a scenario with two hundred people following the system recommendations, the time they take to reach a safe place decreases by 17.7%.
- Multi-agent-based recommender systems: a literature reviewPublication . Neto, Joaquim; Morais, A. Jorge; Gonçalves, Ramiro Manuel Ramos Moreira; Coelho, António LeçaConsidering the growing volume of information and services available on the Web, it has become essential to provide Web sites and applications with tools, such as recommender systems, capable of helping their users to obtain the information and services appropriate to their interests. Due to the complexity of Web adaptation and the ability of multi-agent systems to deal with complex problems, the multi-agent systems technology have been increasing. In this paper, we present a thorough survey of the use of multi-agent-based recommender systems. The results show that the use of multi-agent systems in recommender systems is increasing. The review shows the diversity of applications of multi-agent systems in recommender systems, namely on what concerns to the diversity of domains, different types of approaches, contributing to improving the performance of the recommendation systems.
- Teoria e prática em sistemas de recomendaçãoPublication . Azambuja, Rogério Xavier de; Morais, A. Jorge; Filipe, VítorNas últimas décadas a utilização da inteligência artificial tem sido frequente no desenvolvimento de aplicações computacionais. Mais recentemente a aprendizagem automática, especialmente pelo uso da aprendizagem profunda (deep learning), tem impulsionado o crescimento e ampliado o desenvolvimento de sistemas inteligentes para diferentes domínios. No cenário atual de crescimento tecnológico estão a surgir com maior frequência os sistemas de recomendação (recommender systems) com diferentes técnicas para a filtragem de informações em grandes bases de dados. Um desafio é prover a recomendação adaptativa para mitigar a sobrecarga de informações em ambientes on-line. Este artigo revisa trabalhos anteriores e aborda alguns dos aspectos teórico-conceptuais e teórico-práticos que constituem os sistemas de recomendação, caracterizando o emprego de redes neuronais profundas (Deep Neural Network – DNN) para prover a recomendação sequencial apoiada pela recomendação baseada em sessão.
- Adaptive recommendation in online environmentsPublication . Azambuja, Rogério Xavier de; Morais, A. Jorge; Filipe, VítorRecommender systems form a class of Artificial Intelligence systems that aim to recommend relevant items to the users. Due to their utility, it has gained attention in several applications domains and is high demanded for research. In order to obtain successful models in the recommendation problem in non-prohibitive computational time, different heuristics, architectures and information filtering techniques are studied with different datasets. More recently, machine learning, especially through the use of deep learning, has driven growth and expanded the sequential recommender systems development. This research focuses on models for managing sequential recommendation supported by session-based recommendation. This paper presents the characterization in the specific theme and the state-of-the-art towards study object of the thesis: the adaptive recommendation to mitigate the information overload in online environments.
- X-Wines: a wine dataset for recommender systems and machine learningPublication . Azambuja, Rogério Xavier de; Morais, A. Jorge; Filipe, VítorIn the current technological scenario of artificial intelligence growth, especially using machine learning, large datasets are necessary. Recommender systems appear with increasing frequency with different techniques for information filtering. Few large wine datasets are available for use with wine recommender systems. This work presents X-Wines, a new and consistent wine dataset containing 100,000 instances and 21 million real evaluations carried out by users. Data were collected on the open Web in 2022 and pre-processed for wider free use. They refer to the scale 1–5 ratings carried out over a period of 10 years (2012–2021) for wines produced in 62 different countries. A demonstration of some applications using X-Wines in the scope of recommender systems with deep learning algorithms is also presented.
- A multi-agent system for recommending fire evacuation routes in buildings, based on context and IoTPublication . Neto, Joaquim; Morais, A. Jorge; Gonçalves, Ramiro Manuel Ramos Moreira; Coelho, A. LeçaThe herein proposed research project brings together the area of the multi-agent recommender systems and the IoT and aims to study the extent to which a context-based multi-agent recommender system can contribute to improving efficiency in the evacuation of buildings under a fire emergency, recommending the most adequate and efficient evacuation routes in real time.