Learning Personalized Risk Preferences for Recommendation

The rapid growth of e-commerce has made people accustomed to shopping online. Before making purchases on e-commerce websites, most consumers tend to rely on rating scores and review information to make purchase decisions. With this information, they can infer the quality of products to reduce the risk of purchase. Specifically, items with high rating scores and good reviews tend to be less risky, while items with low rating scores and bad reviews might be risky to purchase. On the other hand, the purchase behaviors will also be influenced by consumers' tolerance of risks, known as the risk attitudes. Economists have studied risk attitudes for decades. These studies reveal that people are not always rational enough when making decisions, and their risk attitudes may vary in different circumstances. Most existing works over recommendation systems do not consider users' risk attitudes in modeling, which may lead to inappropriate recommendations to users. For example, suggesting a risky item to a risk-averse person or a conservative item to a risk-seeking person may result in the reduction of user experience. In this paper, we propose a novel risk-aware recommendation framework that integrates machine learning and behavioral economics to uncover the risk mechanism behind users' purchasing behaviors. Concretely, we first develop statistical methods to estimate the risk distribution of each item and then draw the Nobel-award winning Prospect Theory into our model to learn how users choose from probabilistic alternatives that involve risks, where the probabilities of the outcomes are uncertain. Experiments on several e-commerce datasets demonstrate that our approach can achieve better performance than many classical recommendation approaches, and further analyses also verify the advantages of risk-aware recommendation beyond accuracy

The business digitalization process in SMEs from the implementation of e-commerce: An empirical analysis

©. This manuscript version is made available under the CC-BY 4.0 license http://creativecommons.org/licenses/by/4.0/ This document is the Published, version of a Published Work that appeared in final form in [Journal of Theoretical and Applied Electronic Commerce Research]. To access the final edited and published work see[https://doi.org/10.3390/jtaer18040086]The main objective of this research is to carry out a comprehensive analysis of how e-commerce affects the performance of small and medium-sized enterprises (SMEs) in Mexico. This study will pay special attention to the role of business digitalization and the optimization of operational processes in this context. Our research involved creating a partial least squares structural equation model (PLS-SEM) to examine our hypotheses. According to our research, incorporating e-commerce, digitalizing business processes, and improving operational efficiency significantly contribute to corporate performance. Our results show direct effects that, together with indirect effects of business digitalization and operational efficiency, enhance the positive influence of online commerce. This research fills a gap in the literature by investigating the relationship between ecommerce, business digitalization, operational efficiency, and business performance. It provides essential insights into the direct impact of e-commerce on corporate performance and the indirect impact through the mediation of business digitalization and operational efficiency. The results show significant implications for business managers, as the findings can help them to invest in technologies that foster e-commerce, which, by improving business digitalization and operational efficiency, will result in better corporate performance and the ability to adapt to today’s turbulent environmen

The Business Digitalization Process in SMEs from the Implementation of e-Commerce: An Empirical Analysis.

The main objective of this research is to carry out a comprehensive analysis of how e-commerce affects the performance of small and medium-sized enterprises (SMEs) in Mexico. This study will pay special attention to the role of business digitalization and the optimization of operational processes in this context. Our research involved creating a partial least squares structural equation model (PLS-SEM) to examine our hypotheses. According to our research, incorporating e-commerce, digitalizing business processes, and improving operational efficiency significantly contribute to corporate performance. Our results show direct effects that, together with indirect effects of business digitalization and operational efficiency, enhance the positive influence of online commerce. This research fills a gap in the literature by investigating the relationship between e-commerce, business digitalization, operational efficiency, and business performance. It provides essential insights into the direct impact of e-commerce on corporate performance and the indirect impact through the mediation of business digitalization and operational efficiency. The results show significant implications for business managers, as the findings can help them to invest in technologies that foster e-commerce, which, by improving business digitalization and operational efficiency, will result in better corporate performance and the ability to adapt to today’s turbulent environment

Fundamentos de data science y sus aplicaciones en distintas industrias

Este trabajo de investigación tiene la finalidad de brindar una guía de aprendizaje de los conocimientos, a nivel general, que un profesional debe adquirir con la finalidad de desempeñarse como Data Scientist. A través de este trabajo, se inicia enunciando lo que es Data Science y lo que hace un Data Scientist, y en base a esto discernir cinco categorías de actividades principales. Partiendo de estas cinco actividades se desarrollan los siguientes apartados del primer capítulo, en los que se presentan los conocimientos estadísticos, matemáticos e informáticos que se deben poseer vinculados a cada una de las actividades. Aunque es de mencionar que los conocimientos asociados a estas actividades principales son transversales entre sí para una correcta aplicación del Data Science. También, se debe tener en cuenta que este trabajo solo pretende brindar una pauta para los conocimientos base necesarios para desempeñarse en el área de Data Science, esto implica que no se profundiza en temas relacionados a algoritmos de modelos, de los cuales solo se harán mención por ser relevantes por sus aplicaciones. En el segundo capítulo se mencionan distintas aplicaciones del Data Science en cuatro industrias: servicios de salud, transporte, finanzas y e-commerce. En cada una de estos se muestran distintos casos de aplicación de Data Science entre los que están las predicciones, análisis de decisiones, detecciones de escenarios, optimizaciones, control de sistemas y sistemas de recomendaciones. En cada una de estos casos se refieren de forma concisa los procedimientos seguidos, pasando desde la recolección de los datos hasta el modelo de los mismos, y mencionando los resultados logrados. Finalmente, se presentan conclusiones recabadas de lo que implica una formación como Data Science en la actualidad, así de como su importancia en los campos de aplicación, más ahora, en tiempos donde hay más información disponible y mejores capacidades de cómputo.Trabajo de investigació

The use of machine learning algorithms in recommender systems: A systematic review

The final publication is available at Elsevier via https://doi.org/10.1016/j.eswa.2017.12.020 © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Recommender systems use algorithms to provide users with product or service recommendations. Recently, these systems have been using machine learning algorithms from the field of artificial intelligence. However, choosing a suitable machine learning algorithm for a recommender system is difficult because of the number of algorithms described in the literature. Researchers and practitioners developing recommender systems are left with little information about the current approaches in algorithm usage. Moreover, the development of recommender systems using machine learning algorithms often faces problems and raises questions that must be resolved. This paper presents a systematic review of the literature that analyzes the use of machine learning algorithms in recommender systems and identifies new research opportunities. The goals of this study are to (i) identify trends in the use or research of machine learning algorithms in recommender systems; (ii) identify open questions in the use or research of machine learning algorithms; and (iii) assist new researchers to position new research activity in this domain appropriately. The results of this study identify existing classes of recommender systems, characterize adopted machine learning approaches, discuss the use of big data technologies, identify types of machine learning algorithms and their application domains, and analyzes both main and alternative performance metrics.Natural Sciences and Engineering Research Council of Canada (NSERC) Ontario Research Fund of the Ontario Ministry of Research, Innovation, and Scienc