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- 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.