Um sistema de recomendação de conteúdo suportado pela computação distribuída

TCC (graduação) - Universidade Federal de Santa Catarina. Campus Araranguá. Curso de Tecnologias da Informação e Comunicação.Desde a sua criação, a Internet e mais especificamente a Web, vem passando por grandes modificações. Atualmente, usuários possuem um papel fundamental, não somente consumindo informações, mas também provendo novos conteúdos. Este cenário e os avanços da Tecnologia da Informação tem promovido um aumento vertiginoso no volume de informações disponíveis. A partir disto surgem desafios, entre eles, como permitir que o usuário realize escolhas mais adequadas. Neste contexto, encontram-se os Sistemas de Recomendação com o intuito de auxiliar usuários na tomada de decisão, bem como, a Computação Distribuída como infraestrutura de base para lidar com grandes volumes de informação. A partir disto, o presente trabalho propõe um sistema voltado à recomendação de conteúdo textual através das abordagens de filtragem colaborativa e baseada em conteúdo. Visando permitir a avaliação da proposição deste trabalho foi elaborado um modelo de dados e desenvolvido um protótipo. O protótipo possibilita a geração de informações nas duas principais abordagens de recomendação. Possui ainda a capacidade de realizar o processamento de maneira distribuída. As informações processadas e geradas através da aplicação do protótipo permitem a sugestão de itens, em que no presente trabalho se referem a documentos. Pode-se afirmar que os resultados no que tange a sugestão de conteúdo são consistentes e compatíveis com a literatura da área de Sistemas de Recomendação. Ressalta-se ainda que o desenvolvimento de sistemas distribuídos contribui para área em questão visto que o desempenho frente a grande volumes de informação é fundamental para que se possa produzir insumos que auxiliem usuários em suas escolhas.Since its creation the Internet and more specifically the Web has changed dramatically. Nowadays, users have a key role not only consuming information but also providing new content. This scenario and the advances in Information Technology have fostered the increase in the volume of information available. From this challenges arise, among them, how to allow users to perform more appropriate choices. In this context, there are the Recommender Systems in order to aid users in decision making and Distributed Computing as the base infrastructure to handle large volumes of information. From this, the present work proposes a system towards recommendation of textual content through collaborative filtering and content-based approaches. To allow the evaluation of the proposition a data model has been designed as well as has been developed a prototype. The prototype enables the generation of information on the two major recommendation approaches. It also has the ability to carry out the processing in a distributed manner. The information generated and processed by the prototype allows the suggestion of items which in the present study refers to documents. It can be stated that the results regarding the suggested content are consistent and compatible with the literature in the area of Recommender Systems. It is noteworthy that the development of distributed systems contributes to the area in question since performance against large volumes of information is crucial in order to produce products that can assist users in their choice

Customers’ loyalty model in the design of e-commerce recommender systems

Recommender systems have been adopted in most modern online platforms to guide users in finding more suitable items that match their interests. Previous studies showed that recommender systems impact the buying behavior of e-commerce customers. However, service providers are more concerned about the continuing behavior of their customers, specifically customers’ loyalty, which is an important factor to increase service providers’ share of wallet. Therefore, this study aimed to investigate the customers’ loyalty factors in online shopping towards e-commerce recommender systems. To address the research objectives, a new research model was proposed based on the Cognition-Affect-Behavior model. To validate the research model, a quantitative methodology was utilized to gather the relevant data. Using a survey method, a total of 310 responses were gathered to examine the impacts of the identified factors on customers’ loyalty towards Amazon’s recommender system. Data was analysed using Partial Least Square Structural Equation Modelling. The results of the analysis indicated that Usability (P=0.467, t=5.139, p<0.001), Service Interaction (P=0.304, t=4.42, p<0.001), Website Quality (P=0.625, t=15.304, p<0.001), Accuracy (P=0.397, t=6.144, p<0.001), Novelty (P=0.289, t=4.406, p<0.001), Diversity (P=0.142, t=2.503, p<0.001), Recommendation Quality (P=0.423, t=7.719, p<0.001), Explanation (P=0.629, t=15.408, p<0.001), Transparency (P=0.279, t=5.859, p<0.001), Satisfaction (P=0.152, t=3.045, p<0.001) and Trust (P=0.706, t=14.14, p<0.001) have significant impacts on customers’ loyalty towards the recommender systems in online shopping. Information quality, however, did not affect the quality of the website that hosted the recommender system. The findings demonstrated that accuracy-oriented measures were insufficient in understanding customer behavior, and other quality factors, such as diversity, novelty, and transparency could improve customers’ loyalty towards recommender systems. The outcomes of the study indicated the significant impact of the website quality on customers’ loyalty. The developed model would be practical in helping the service providers in understanding the impacts of the identified factors in the proposed customers’ loyalty model. The outcomes of the study could also be used in the design of recommender systems and the deployed algorithm