Concepção e Desenvolvimento de um Sistema de Recomendação para o Varejo Físico

TCC(graduação) - Universidade Federal de Santa Catarina. Centro Tecnológico. Engenharia de Controle e Automação.O setor de varejo físico é um dos mais representativos para a economia brasileira e vem enfrentando novos desafios traçados pela atual revolução digital. Neste contexto, o seguinte trabalho realiza a concepção e o desenvolvimento de um sistema de recomendação focado no setor de varejo físico. Inicialmente é levantado os requisitos deste tipo de sistema bem como as abordagens de recomendação que melhor se adequam ao cenário. Após, é desenvolvido uma proposta de arquitetura de sistema de recomendação ideal para varejo físico, baseado no método de ensemble learning. É realizada a implementação desta arquitetura sobre uma base de dados da empresa Grupo Soma®. Os resultados da implementação foram medidos, tendo um RMSE de 98,9% e foram considerados satisfatórios, visto que conseguiram cumprir totalmente 8 dos 11 requisitos levantados. Dessa forma, foi possível demonstrar a viabilidade da implantação de um sistema de recomendação dentro do varejo físico.The physical retail sector is one of the most representative for the Brazilian economy and has been facing new challenges outlined by the current digital revolution. In this context, the following work conceives and develops a recommendation system focused on the physical retail sector. Initially the requirements for this type of system are raised as well as the recommendation approaches that best fit the scenario. Afterwards, a proposal of ideal recommendation system architecture for physical retail, based on the ensemble learning method, is developed. The implementation of this architecture is carried out on a database of the company Grupo Soma®. The results of the implementation were measured, with a RMSE of 98.9% and were considered satisfactory, as they were able to fully comply with 8 of the 11 requirements raised. In this way, it was possible to demonstrate the feasibility of implementing a recommendation system within the physical retail

Comparativa entre regresión logística ordinal, redes neuronales artificiales y Gradient boosting; en la predicción de la satisfacción laboral en Ecuador

La presente investigación tiene como objetivo comparar la calidad predictiva y la demanda de procesamiento de la técnica clásica: regresión logística ordinal y las técnicas de machine learning: redes neuronales artificiales y gradient boosting. El estudio se plantea en un contexto donde el avance tecnológico ha permitido un crecimiento exponencial en la producción de información, la cual requiere ser analizada de forma eficiente, por lo tanto, resulta indispensable identificar las mejores técnicas para el análisis. La comparación se realizó en el marco de la construcción de un modelo que prediga el nivel de satisfacción laboral en jefes de hogar ecuatorianos con un único trabajo. Así, se estudiaron las principales características de ambas metodologías y se identificaron sus equivalencias en terminología. Posteriormente se realizó una comparación cuantitativa de la calidad predictiva, tiempos de procesamiento y pico de memoria RAM asociados a cada uno de los modelos construidos con las tres técnicas, se realizó un proceso de remuestreo mediante ten-fold cross validation y se corrieron 200 modelos por cada técnica para controlar la variabilidad propia del fenómeno bajo estudio. Finalmente se contrastó el nivel de procesamiento generado, tomando en cuenta dos factores: 1) tamaño de muestra (real y aumentada con 37 336 y 373 360 observaciones, respectivamente), y 2) número de núcleos efectivos del procesador (uno y siete). Los resultados mostraron que el error total de predicción para gradient boosting fue del 29.5%, concluyendo así que esta técnica es la más confiable en su tarea predictiva, presentando una alta demanda de procesamiento, la cual disminuye considerablemente al trabajar en paralelo, es decir, al utilizar todos los núcleos del procesador. Se recomienda utilizar gradient boosting en estudios socio – económicos similares al estudio aquí planteado.This research aims to compare the predictive quality and processing demand of the classical technique: ordinal logistic regression and machine learning techniques: artificial neural networks and gradient boosting. The study is set in a context where technological progress has allowed exponential growth in the production of information, which needs to be analyzed efficiently, therefore, it is essential to identify the best techniques for analysis. The comparison was made within the framework of the construction of a model that predicts the level of job satisfaction in Ecuadorian householders with a single job. Therefore, the main characteristics of both methodologies were studied and their equivalences in terminology were identified. Subsequently, a quantitative comparison of the predictive quality was made, processing times and peak RAM associated with each of the models built with the three techniques, a resampling process was performed using ten-fold cross validation and 200 models were run per each technique to control the variability of the phenomenon under study. Finally, the level of processing generated was contrasted, taking into account two factors: 1) sample size (real and increased with 37 336 and 373 360 observations, respectively), and 2) number of effective processor cores (one and seven). The results showed that the total prediction error for gradient boosting was 29.5%, concluding that this technique is the most reliable in its predictive task, presenting a high demand for processing, which decreases considerably when working in parallel, that is, when using all processor cores. It is recommended to use gradient boosting in socio-economic studies like the study proposed here

MACHINE LEARNING ASSISTED STRATEGIC SYNTHESIS OF TISSUE MIMETIC ELASTOMERS

Over the course of evolution, biological creatures in nature have developed various elegant mechanisms to defend themselves. Particularly, soft biological tissues not only serve as cushions but at the same time, also prevent tearing. Meanwhile, some tissues, such as the skin of chameleons, can also display adaptive coloration which protects them from predators and helps them attract spouses. Inspired by the multifunctionality of biological tissues, this study focused on developing materials that possess a combination of these unique properties. To characterize the nonlinear elasticity of tissues and synthetic materials that mimic this property, we used firmness β and Young’s modulus E_0. To unravel the origin of mechanical properties of tissues, we studied the stress-strain curves of previously measured tissues from literatures. We demonstrated that the mechanical properties of tissues were tied to their functions and structural organization of collagens. To target the nonlinear elasticity synthetically, we used linear-bottlebrush-linear (LBL) triblock copolymers that micro-phase separate into physical networks, which we named plastomers. The triblock was produced by a two-step atomic transfer radical polymerization (ATRP) synthesis: the bottlebrush macroinitiator was synthesized by grafting-through polymerization followed by linear chain extension from both ends of the macroinitiator. The synthetic challenges and synthetic outcomes on the effect of mechanical properties of plastomers were investigated. Rigorous kinetic studies were performed to optimize the synthetic conditions for producing bottlebrush macroinitiator with high chain end fidelity. Next, we investigated in the control of mechanical properties by varying architectural parameters as well as mixing experiments. We showed that there is still a gap between synthetic plastomers and biological tissues. In particular, we lacked synthetic materials that possessed high firmness (β > 0.8) and high modulus (E_0 > 105 Pa). To bridge this gap, we needed to target plastomers with specific firmness and modulus. Therefore, we developed statistical and machine learning models that predicted the mechanical properties of triblocks based on chemical and architectural parameters. Finally, we investigated in incorporating structural coloration into plastomers. We studied factors, such as architectural parameters of the plastomers and swelling that controlled the reflected color of the plastomers. Specifically, we utilized ultraviolet-visible (UV-VS) spectroscopy and small angle X-ray scattering (SAXS) to demonstrate the effect of these factors on reflected wavelength and periodicity of the plastomers.Doctor of Philosoph