Navegando por Autor "Silva, Douglas Véras e"

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Análise de um sistema de recomendação de restaurantes sensível ao contexto sobre o grau de satisfação dos usuários
(2023-09-01) Melo Filho, Carlos Olimpio Rodrigues de; Silva, Douglas Véras e; http://lattes.cnpq.br/2969243668455081; http://lattes.cnpq.br/6986499479035317
Popular applications of recommender systems can be found in many areas. In the food business, platforms such as TripAdvisor stand out for suggesting specialized restaurant recommendations based on various types of relevant information, such as reviews from other users for the menu, atmosphere and recommendations for the closest restaurants are some of the specialties of these platforms. With the possibility of using new data sensitive to the user’s context, the main objective of this work is to evaluate the usage of the reason of going to the restaurant to reorganize the final restaurants recommendation through a context-based post-filtering. To achieve the goal, a mobile application was developed, the SR Recife Restaurants, to assess the degree of satisfaction of real users to the recommended restaurants, an online evaluation approach, using questionnaires, was used. When carrying out the experiment with 15 users, it was possible to notice an increase of 26.67% in the degree of satisfaction of the top-5 first recommendations when using the trip type to the restaurant as context data for the post-filtering phase.
Avaliação entre algoritmos de filtragem colaborativa baseada em vizinhança e transferência de conhecimento para CD-CARS
(2019) Silva, Guilherme Melo da; Silva, Douglas Véras e; http://lattes.cnpq.br/2969243668455081; http://lattes.cnpq.br/7122596102314881
Recommendations in scenarios with the lack of preferences expressed by users is an importantlimitation for Recommendation Systems (RS). Due to this problem, cross-domain RS (CDRS)searches have gained relevance, where collaborative filtering (CF) is one of the most exploitedtechniques in this area. The CD-CARS system shows that the use of contextual information,available in user preferences, can optimize CF neighborhood-based algorithms, a techniquewidely used in multidomain CF. Although they provide accurate recommendations, some neigh-borhood-based algorithms such as the one used in the CD-CARS have the limitation of the useof multi-domains only in the occurrence of user overlap between domains, a non-trivial scenarioin real databases. This work presents a comparative analysis of different recommendation algo-rithms involving collaborative filtering techniques. The CD-CARS’ NNUserNgbr-transClosure(CF neighborhood-based) and Tracer (CF transfer learning-based) algorithms, were used as thebasis for the recommendation algorithms. In the experiments, the CF algorithms were integratedinto the context-aware techniques, addressed in the CD-CARS: Contextual Pre-Filtering andPost-Filtering, being applied on two data sets, formed by two auxiliary domains and one target,with and without overlap between domains. The MAE and RMSE performance metrics wereused to evaluate the algorithms. The results of the experiments showed that the Tracer algorithmpresented better results concerning the NNUserNgbr-transClosure algorithm in all experimentscenarios without user overlap, with and without the use of the Contextual Pre-Filtering or Post-Filtering.
Comparison of recommendation algorithms for user groups: a food-based case study
(2023-04-24) Vasconcelos, Caio Giovanni Pereira; Silva, Douglas Véras e; http://lattes.cnpq.br/2969243668455081; http://lattes.cnpq.br/4775036700843482
Empreendedorismo digital e a importância do estudo do consumidor para o planejamento estratégico: estudo de caso da Foodrec
(2025-03-19) Maranhão, Stéphanie Samara de Lima; Leão, Éder Lira de Souza; Silva, Douglas Véras e; http://lattes.cnpq.br/2969243668455081; http://lattes.cnpq.br/4434499456331867; http://lattes.cnpq.br/3239983602229233
A pesquisa em questão investiga a relevância do estudo do consumidor no planejamento estratégico de negócios digitais, com foco na startup FoodRec. Em um cenário de constantes inovações tecnológicas, compreender o comportamento do consumidor tornou-se um fator determinante para o sucesso das empresas, especialmente no ambiente das redes sociais. Para isso, a pesquisa adota uma abordagem qualitativa por meio de um estudo de caso, analisando dados de campanhas de marketing digital realizadas no Instagram e TikTok. Foram exploradas tanto campanhas orgânicas quanto pagas, com o objetivo de identificar padrões de consumo e características do perfil do público-alvo. Os resultados indicam que o mapeamento sobre o consumidor contribui para a formulação de estratégias mais assertivas, impactando diretamente a tomada de decisão e a eficácia das campanhas publicitárias. Conclui-se que o uso inteligente dos dados das redes sociais permite aprimorar o engajamento, personalizar ações que otimizem a conversão de consumidores no ambiente digital.
Estudo comparativo de técnicas de seleção de contextos em sistemas de recomendação de domínio cruzado sensivéis ao contexto
(2018) Brito, Victor Sales de; Silva, Douglas Véras e; http://lattes.cnpq.br/2969243668455081; http://lattes.cnpq.br/0340874538265508
There are several approaches to implement a recommendation system, such as CrossDomain Context-Aware Recommendation Systems (CD-CARS), which was used in this work because it enables quality improvement of recommendations using multiple domains (e.g. books, movies and musics), while taking into account the use of contexts (e.g. season, time, company and location). However, caution is needed in using contexts to make items suggestions, since the contexts may impair the recommendation performance when they are considered “irrelevants”. Therefore, the selection of relevant contexts is a key factor for the development of CD-CARS, and there is a lack of papers for selection techniques in datasets with contextual information and cross-domain. Thus, this work applied the Information Gain (IG), Chi-square test, Minimum Redundancy Maximum Relevance (MRMR) and Monte Carlo Feature Selection (MCFS) techniques in twelve datasets with three diﬀerent contextual dimensions (time, location and company) and distinct domains (books, television and musics). Finally, from the results obtained, the MCFS technique was able to classify the relevance of the contexts in a more satisfactory way than other techniques.
Um estudo comparativo de técnicas para a classificação contextual de companhia para sistemas de recomendação sensíveis a contexto
(2019-01-22) Silva, Douglas Henrique Santana da; Silva, Douglas Véras e; http://lattes.cnpq.br/2969243668455081; http://lattes.cnpq.br/6428879549861854
Nowadays, the vast amount of information has harmed users during decision making. In face of this problem, recommendation systems have been proposed in order to offer suggestions that help users to overcome such problem. These suggestions are even more valuable when these systems begin to suggest items based on the user contexts. Among these contexts, the companion context can be highlighted. Through the inference of the companion context the system may suggest different items if the user is accompanied or not. An example of a system that has such features is the CD-CARS. However, the unsupervised learning method for companion inference on CD-CARS has some limitations. In this way, the present research analyzed and highlighted a supervised learning method that can replace the current company contextual classification approach executed in the CD-CARS.
Estudo de viabilidade de sistemas de detecção de armamentos em tempo real em linhas de ônibus urbanos
(2021-12-09) Lima Junior, Cícero Pereira de; Silva, Douglas Véras e; http://lattes.cnpq.br/2969243668455081; http://lattes.cnpq.br/9901763283774954
Surveillance systems are fundamental on preventing armed robberys on public busses. However, to be operated in real-time theses systems demand an unrealistic amount of people. The usage of computer vision and deep learning technics raises as a way to automate parts or even the whole surveillance process, from the weapons detection to the alarm triggering. For this process to be accomplished efficiently, allowing authorities to take more effective actions, the system needs to be able to handle a growing security cameras demand. Thus, this work analyses a bus line weapon detection system viabillity. Through simulation, this work evaluated the perfomance of YOLO algorithm, in its fourth version, on a client-server model under a growing security camera demand. The server is composed of a Tesla V80 GPU with a 12GB memory, Intel Xeon dual core processor, 61GB RAM memory and 200GB disk space. Finally, from the gathered results, its observable that the application presents a detection time increase after having introduced 16 virtual users (cameras), also the average response time cannot be considered as real-time, on bus security context.
Explainable Artificial Intelligence - uma análise dos trade-offs entre desempenho e explicabilidade
(2023-08-18) Assis, André Carlos Santos de; Andrade, Ermeson Carneiro de; Silva, Douglas Véras e; http://lattes.cnpq.br/2969243668455081; http://lattes.cnpq.br/2466077615273972; http://lattes.cnpq.br/3963132175829207
Explainability is essential for users to efficiently understand, trust, and manage computer systems that use artificial intelligence. Thus, as well as assertiveness, understanding how the decision-making process of the models occurred is fundamental. While there are studies that focus on the explainability of artificial intelligence algorithms, it is important to highlight that, as far as we know, none of them have comprehensively analyzed the trade-offs between performance and explainability. In this sense, this research aims to fill this gap by investigating both transparent algorithms, such as Decision Tree and Logistic Regression, and opaque algorithms, such as Random Forest and Support Vector Machine, in order to evaluate the trade-offs between performance and explainability. The results reveal that opaque algorithms have a low explanability and do not perform well regarding response time due to their complexity, but are more assertive. On the other hand, transparent algorithms have a more effective explainability and better performance regarding response time, but in our experiments, we observed that accuracy obtained was lower than the accuracy of opaque models.
Melhorando recomendações baseadas no contexto com classificação de texto para sistemas de recomendação de domínios cruzados
(2023-09-21) Liu, Jen Horng; Silva, Douglas Véras e; Pacífico, Luciano Demétrio Santos; http://lattes.cnpq.br/9521600706234665; http://lattes.cnpq.br/2969243668455081; http://lattes.cnpq.br/7294715956605990
Os sistemas de recomendação sensíveis ao contexto (CARS) ganharam atenção substancial por sua capacidade de melhorar a precisão das recomendações, considerando vários fatores contextuais. Contudo, a integração eficaz de informações contextuais em diversos domínios continua a ser um desafio. Este artigo apresenta uma nova metodologia para inferir informações contextuais usando técnicas de classificação de texto, e avalia seu impacto no desempenho de um Sistema de Recomendação Sensível ao Contexto de Domínios Cruzados (CD-CARS). Nossa metodologia envolve o préprocessamento de dados textuais e a utilização de Support Vector Machines (SVM) para inferência de contexto. Através de uma avaliação extensa, analisamos os efeitos da integração do contexto inferido nos algoritmos do CD-CARS. Os resultados experimentais demonstram que a metodologia proposta produz maior precisão e relevância das recomendações em diferentes domínios. As conclusões destacam o potencial de aproveitar técnicas de classificação de texto para melhorar as recomendações sensíveis ao contexto, contribuindo assim para o avanço dos sistemas de recomendação em cenários de domínios cruzados.