Effect of timing and source of online product recommendations: An eye-tracking study

Online retail business has become an emerging market for almost all business owners. Online recommender systems provide better services to the consumers as well as assist consumers with their decision making processes. In this study, a controlled lab experiment was conducted to assess the effect of recommendation timing (early, mid, and late) and recommendation source (expert reviews vs. consumer reviews) on e-commerce users\u27 interest and attention. Eye-tracking data was extracted from the experiment and analyzed. The results suggest that users show more interest in recommendation based on consumer reviews than recommendation based on expert reviews. Earlier recommendations do not receive greater user attention than later recommendations --Abstract, page iii

Algoritmo de recomendação de presentes em dispositivos móveis

Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Faculdade UnB Gama, 2017.O crescimento do mercado de aplicações para dispositivos móveis e de vendas do mercado de e-commerce vem aumentado constantemente, aliado com o crescimento de estudos e soluções de recomendação de produtos implementados nos sistemas e-commerce. Neste contexto, este Trabalho de Conclusão de Curso propõe um algoritmo de recomendação de presentes em dispositivos móveis baseado no framework COREL. O algoritmo de recomendação proposto é uma customização do COREL, tomando como base a complexidade de implementação associada à aplicativos móveis para iOS. Portanto, este trabalho objetiva customizar um algoritmo de recomendação de presentes no contexto de dispositivos móveis utilizando como insumo principal as preferências do usuário para a recomendação de presentes no aplicativo Giftr.The mobile application market and the sales of the e-commerce have been growing steadily, also with the growth of studies and products recommendation solutions implemented in the e-commerce systems. In this context, this paper proposes a recommendation algorithm for mobile devices based on the COREL framework. The proposed recommendation algorithm is a customization of the COREL framework, based on the implementation complexity associated with iOS mobile applications. Therefore, this work aims to customize a gift recommendation algorithm in the context of mobile devices using as main input the user preferences for the recommendation of gifts in the Giftr application