Recommending Best Products from E-commerce Purchase History and User Click Behavior Data

E-commerce collaborative filtering recommendation systems, the main input data of user-item rating matrix is a binary purchase data showing only what items a user has purchased recently. This matrix is usually sparse and does not provide a lot of information about customer purchases or product clickstream behavior (eg., clicks, basket placement, and purchase) history, which possibly can improve product recommendations accuracy. Existing recommendation systems in E-commerce with clickstream data include those referred in this thesis as Kim05Rec, Kim11Rec, and Chen13Rec. Kim05Rec forms a decision tree on click behavior attributes such as search type and visit times, discovers the possibility of a user putting products into the basket and uses the information to enrich the user-item rating matrix. If a user clicked a product, Kim11Rec then finds the associated products for it in three stages such as click, basket and purchase, uses the lift value from these stages and calculates a score, it then uses the score to make recommendations. Chen13Rec measures the similarity of users on their category click patterns such as click sequences, click times and visit duration; it then can use the similarity to enhance the collaborative filtering algorithm. However, the similarity between click sequences in sessions can apply to the purchases to some extent, especially for sessions without purchases, this will be able to predict purchases for those session users. But the existing systems have not integrated it, or the historical purchases which shows more than whether or not a user has purchased a product before. In this thesis, we propose HPCRec (Historical Purchase with Clickstream based Recommendation System) to enrich the ratings matrix from both quantity and quality aspects. HPCRec firstly forms a normalized rating-matrix with higher quality ratings from historical purchases, then mines consequential bond between clicks and purchases with weighted frequencies where the weights are similarities between sessions, but rating quantity is better by integrating this information. The experimental results show that our approach HPCRec is more accurate than these existing methods, HPCRec is also capable of handling infrequent cases whereas the existing methods can not

O impacto da inteligência artificial no negócio eletrónico

Pela importância que a Inteligência Artificial exibe na atualidade, revela-se de grande interesse verificar até que ponto ela está a transformar o Negócio Eletrónico. Para esse efeito, delineou-se uma revisão sistemática com o objetivo de avaliar os impactos da proliferação destes instrumentos. A investigação empreendida pretendeu identificar artigos científicos que, através de pesquisas realizadas a Fontes de Dados Eletrónicas, pudessem responder às questões de investigação implementadas: a) que tipo de soluções, baseadas na Inteligência Artificial (IA), têm sido usadas para melhorar o Negócio Eletrónico (NE); b) em que domínios do NE a IA foi aplicada; c) qual a taxa de sucesso ou fracasso do projeto. Simultaneamente, tiveram de respeitar critérios de seleção, nomeadamente, estar escritos em inglês, encontrarem-se no intervalo temporal 2015/2021 e tratar-se de estudos empíricos, suportados em dados reais. Após uma avaliação de qualidade final, procedeu-se à extração dos dados pertinentes para a investigação, para formulários criados em MS Excel. Estes dados estiveram na base da análise quantitativa e qualitativa que evidenciaram as descobertas feitas e sobre os quais se procedeu, posteriormente, à sua discussão. A dissertação termina com as conclusão e discussão de trabalhos futuros.Due to the importance that Artificial Intelligence exhibits today, it is of great interest to see to what extent it is transforming the Electronic Business. To this end, a systematic review was designed to evaluate the impacts of the proliferation of these instruments. The research aimed to identify scientific articles that, through research carried out on Electronic Data Sources, could answer the research questions implemented: a) what kind of solutions, based on Artificial Intelligence, have been used to improve the Electronic Business; b) in which areas of the Electronic Business Artificial Intelligence has been applied; c) what the success rate or failure of the project is. At the same time, they must comply with selection criteria, to be written in English, to be found in the 2015/2021-time interval and to be empirical studies supported by actual data. After a final quality evaluation, the relevant data for the investigation were extracted for forms created in MS Excel. These data were the basis of the quantitative and qualitative analysis that evidenced the findings found and on which they were subsequently discussed. The dissertation ends with the conclusion and discussion of future works

Variables estratégicas para pronosticar resultados financieros en pequeñas empresas a través de redes neuronales y árboles de decisión

Esta tesis pretende analizar la posibilidad de crear modelos predictivos estratégicos utilizando redes neuronales y árboles de decisión. Los modelos deben permitir el pronóstico del impacto de las estrategias en los resultados financieros. Los modelos se crearán a partir de datos obtenidos mediante cuestionarios a gerentes sobre las estrategias aplicadas en sus organizaciones y a partir de los datos financieros de las organizaciones declaradas al estado portugués. La posibilidad de pronosticar el impacto de las estrategias en los resultados financieros puede significar una ventaja significativa, ya que estos modelos permiten una verificación del impacto antes de la asignación efectiva de recursos en la ejecución de las estrategias. Los modelos resultantes son objecto de un estudio sobre su desempeño predictivo. La conclusión del estudio se refiere a la metodología utilizada como muy prometedora para los objetivos para los que se propuso, determinando un error promedio máximo del 25% y con un error máximo del 30% en más del 70% del conjunto de muestras para evaluación.This thesis intends to analyze the possibility of creating strategic predictive models using neural networks and decision trees. The models should allow the forecast of the impact of the strategies on the financial results. The models are created from data obtained by questionnaire to managers about the strategies applied in their organizations and from the financial data of the organizations declared to the Portuguese state. The possibility of forecasting the impact of the strategies on the financial results can mean a significant advantage, since these models allow a verification of the impact before the effective allocation of resources in the execution of the strategies. The resulting models are the subject of a study on their predictive performance. The achieved results are very prommising. The conclusion of the study refers to the methodology used as very promising for the objectives it was proposed for, determining a maximum average error of 25% and with a maximum error of 30% in more than 70% of the sample set for evaluation.Pretende-se com esta tese analisar a possibilidade de criar modelos preditivos estratégicos utilizando redes neuronais e árvores de decisão. Os modelos deverão permitir a previsão do impacto das estratégias nos resultados financeiros. Os modelos são criados a partir de dados obtidos por questionário aos gestores acerca das estratégias aplicadas nas suas organizações e a partir dos dados financeiros das organizações declarados ao estado português. A possibilidade de previsão do impacto das estratégias nos resultados financeiros pode significar uma vantagem significativa, uma vez que estes modelos permitem uma verificação do impacto antes da alocação efetiva de recursos na execução das estratégias. Os modelos resultantes são alvos de um estudo sobre o seu desempenho preditivo. A conclusão do estudo refere a metodologia utilizada como muito promissora para os objetivos a que se propôs, determinando um erro médio máximo de 25% e com um erro máximo de 30% em mais de 70% do conjunto amostral para avaliação

Intention-Based Online Consumer Classification for Recommendation and Personalization. Hot Topics in Web Systems and Technologies

International audienceConsumers' online shopping behaviors are mostly determined by their intentions. Thus, the knowledge of consumer intention can help online marketers to enhance sales conversion rate and reduce ineffective marketing communications. Current personalization and recommendation techniques do not pay enough attention to various consumer intentions. The taxonomy of online shopping intention and the method to predict intention in real time are yet to be developed. Based on unsupervised and supervised learning techniques, this paper proposes an intention prediction model to fulfill the research gap. Empirical results suggest that the proposed model is able to classify intentions precisely. Accordingly, we discuss the implication and provide some managerial suggestions to online marketers who seek to implement some intention-based personalization methods