Using deep learning for ordinal classification of mobile marketing user conversion

In this paper, we explore Deep Multilayer Perceptrons (MLP) to perform an ordinal classification of mobile marketing conversion rate (CVR), allowing to measure the value of product sales when an user clicks an ad. As a case study, we consider big data provided by a global mobile marketing company. Several experiments were held, considering a rolling window validation, different datasets, learning methods and performance measures. Overall, competitive results were achieved by an online deep learning model, which is capable of producing real-time predictions.This article is a result of the project NORTE-01-0247-FEDER-017497, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). This work was also supported by Funda¸c˜ao para a Ciˆencia e Tecnologia (FCT) within the Project Scope: UID/CEC/00319/201

An empirical study on anomaly detection algorithms for extremely imbalanced datasets

Anomaly detection attempts to identify abnormal events that deviate from normality. Since such events are often rare, data related to this domain is usually imbalanced. In this paper, we compare diverse preprocessing and Machine Learning (ML) state-of-the-art algorithms that can be adopted within this anomaly detection context. These include two unsupervised learning algorithms, namely Isolation Forests (IF) and deep dense AutoEncoders (AE), and two supervised learning approaches, namely Random Forest and an Automated ML (AutoML) method. Several empirical experiments were conducted by adopting seven extremely imbalanced public domain datasets. Overall, the IF and AE unsupervised methods obtained competitive anomaly detection results, which also have the advantage of not requiring labeled data.This work has been supported by the European Regional Development Fund (FEDER) through a grant of the Operational Programme for Competitivity and Internationalization of Portugal 2020 Partnership Agreement (PRODUTECH4S&C, POCI-01-0247-FEDER-046102)

Predicting how much a consumer is willing to pay for a bottle of wine: a preliminary study

The wine industry is an important business sector, generating billions in annual revenue. In the last year, there were several lockdowns due to the COVID-19 pandemic and wine consumption at home has increased. This paper considers the problem of predicting how much a consumer is willing to pay for a bottle of wine to drink at home, in a regular occasion. As far as we know, this is the first study on the subject. The problem is treated as a classification task and several prediction models, based on artificial neural networks, support vector machines and decisions trees, are proposed and compared.publishe

AI4CITY - An automated machine learning platform for smart cities

Nowadays, the general interest in Machine Learning (ML) based solutions is increasing. However, to develop and deploy a ML solution often requires experience and it involves developing large code scripts. In this paper, we propose AI4CITY, an automated technological platform that aims to reduce the complexity of designing ML solutions, with a particular focus on Smart Cities applications. We compare our solution with popular Automated ML (AutoML) tools (e.g., H2O, AutoGluon) and the results achieved by AI4CITY were quite interesting and competitive.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020, and was carried out within the project ”City Catalyst - Catalisador para Cidades Sustentáveis” reference POCI/LISBOA-01-0247-FEDER-046119, co-funded by Fundo Europeu de Desenvolvimento Regional (FEDER), through Portugal 2020 (P2020)

Predictive Customer Lifetime value modeling: Improving customer engagement and business performance

CookUnity, a meal subscription service, has witnessed substantial annual revenue growth over the past three years. However, this growth has primarily been driven by the acquisition of new users to expand the customer base, rather than an evident increase in customers' spending levels. If it weren't for the raised subscription prices, the company's customer lifetime value (CLV) would have remained the same as it was three years ago. Consequently, the company's leadership recognizes the need to adopt a holistic approach to unlock an enhancement in CLV. The objective of this thesis is to develop a comprehensive understanding of CLV, its implications, and how companies leverage it to inform strategic decisions. Throughout the course of this study, our central focus is to deliver a fully functional and efficient machine learning solution to CookUnity. This solution will possess exceptional predictive capabilities, enabling accurate forecasting of each customer's future CLV. By equipping CookUnity with this powerful tool, our aim is to empower the company to strategically leverage CLV for sustained growth. To achieve this objective, we analyze various methodologies and approaches to CLV analysis, evaluating their applicability and effectiveness within the context of CookUnity. We thoroughly explore available data sources that can serve as predictors of CLV, ensuring the incorporation of the most relevant and meaningful variables in our model. Additionally, we assess different research methodologies to identify the top-performing approach and examine its implications for implementation at CookUnity. By implementing data-driven strategies based on our predictive CLV model, CookUnity will be able to optimize order levels and maximize the lifetime value of its customer base. The outcome of this thesis will be a robust ML solution with remarkable prediction accuracy and practical usability within the company. Furthermore, the insights gained from our research will contribute to a broader understanding of CLV in the subscription-based business context, stimulating further exploration and advancement in this field of study

Predicting yarn breaks in textile fabrics: a machine learning approach

In this paper, we propose a Machine Learning (ML) approach to predict faults that may occur during the production of fabrics and that often cause production downtime delays. We worked with a textile company that produces fabrics under the Industry 4.0 concept. In particular, we deal with a client customization requisite that impacts on production planning and scheduling, where there is a crucial need of limiting machine stoppage. Thus, the prediction of machine stops enables the manufacturer to react to such situation. If a specific loom is expected to have more breaks, several measures can be taken: slower loom speed, special attention by the operator, change in the used yarn, stronger sizing recipe, etc. The goal is to model three regression tasks related with the number of weft breaks, warp breaks, and yarn bursts. To reduce the modeling effort, we adopt several Automated Machine Learning (AutoML) tools (H2O, AutoGluon, AutoKeras), allowing us to compare distinct ML approaches: using a single (one model per task) and Multi-Target Regression (MTR); and using the direct output target or a logarithm transformed one. Several experiments were held by considering Internet of Things (IoT) historical data from a Portuguese textile company. Overall, the best results for the three tasks were obtained by the single-target approach with the H2O tool using logarithm transformed data, achieving an R2 of 0.73 for weft breaks. Furthermore, a Sensitivity Analysis eXplainable Artificial Intelligence (SA XAI) approach was executed over the selected H2OAutoML model, showing its potential value to extract useful explanatory knowledge for the analyzed textile domain.This work is supported by the European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project PPC4.0 - Production Planning Control 4.0; Funding Reference: POCI-01-0247-FEDER-069803]

Send frequency prediction on email marketing

O E-mail Marketing é uma forma de marketing direta que utiliza o e-mail como um meio de comunicação comercial pelo que numa perspetiva mais ampla, qualquer e-mail enviado a um potencial subscritor e atuais subscritores também pode ser considerado e-mail marketing. Assim sendo, o subscritor vai receber várias comunicações ao longo do dia, reduzindo a visibilidade dos e-mails mais antigos com a entrada de novas comunicações e consequentemente, reduzindo as taxas de aberturas. Tendo em conta que existem subscritores que preferem abrir e ler as suas comunicações de manhã, outros de tarde e alguns durante a noite, é necessário enviar uma comunicação que proporcione uma maior visibilidade que perpetue maiores taxas de abertura e uma maior captação de interesse do subscritor com a entidade que enviou uma comunicação. Esta tese apresenta uma solução para enviar comunicações de marketing na altura certa aos subscritores ou potenciais subscritores. A sua contribuição consiste num modelo segmentado que utiliza um algoritmo tradicional de clustering baseado na informação trocada entre as empresas e os seus subscritores. O modelo implementa posteriormente uma abordagem de ensemble paralelo utilizando técnicas como simple averaging e stacking com algoritmos de regressão treinados (RF, Linear Regression, KNN e SVR) e com um algoritmo de deep learning (RNNs) para determinar a melhor altura para enviar comunicações de e-mail. A implementação é executada utilizando um dataset fornecido pela empresa E-goi para treinar e testar a abordagem mencionada. Os resultados obtidos nesta tese indicam que o algoritmo KNN é mais adequado para prever o melhor momento para enviar comunicações de e-mail dos algoritmos ML treinados. Das duas técnicas utilizadas para a abordagem do ensemble paralelo, o stacking é o mais adequado para prever o melhor momento para o envio das comunicações de e-mail.Email Marketing is a form of direct marketing that uses email as a means of commercial communication. In a broader perspective, any email sent to a potential subscriber and current subscribers can also be considered email marketing. Therefore, the subscriber will receive several communications throughout the day, reducing the visibility of older emails with the entry of new communications and consequently reducing open rates. Considering that there are subscribers who prefer to open and read their communications in the morning, others in the afternoon, and some at night, it is necessary to send a communication that provides the visibility that leads to higher open rates and capture the subscribers’ interest with the entity that sent the communication. This thesis presents a solution to send marketing communications at the right time to subscribers or potential subscribers. Its contribution consists of a segmented model that uses a traditional clustering algorithm based on the information exchanged between companies and subscribers. The model then implements a parallel ensemble approach using simple averaging and stacking techniques with trained regression algorithms (RF, Linear Regression, KNN, and SVR) and a deep learning algorithm (RNNs) to determine the best time to send email communications. The implementation is executed using a dataset provided by the company E-goi to train and test the mentioned approach. The results obtained in this thesis indicate that the KNN algorithm is better suited to predict the best time to send email communications of all the trained ML algorithms. Stacking is the most suitable for predicting the best time to send email communications of the two techniques used for the parallel ensemble approach

The Impact of Positive Online Review Tags on Snacks Sales: A Case of Bestore in Tmall

Customers’ reviews in e-commerce sites play a significant role in influencing potential customers’ purchasing decisions which ultimately affects products sales. Chinese e-commerce sites like Tmall, Taobao and JD.com contain a collection of aspect tags that group reviews with similar comments tags to help customers browse reviews and evaluate products more conveniently. To validate whether these tags are useful and actually playing a role in promoting future sales, we collected data including product information and review tags on a regular basis for consecutive 8 weeks from Bestore, a snack seller on Tmall. We classified the collected review tags into 9 types based on their semantic meanings. Finally, we analyzed and performed generalized estimating equations (GEE) modeling on the data set consisting of 234 products with a total of 734 tags. The results show that most of the aspect tags are related to immediate period sales volume and certain tags are more capable of nowcasting next immediate sales

MANÜEL ÖZNİTELİK ÇIKARIMI VE DERİN ÖĞRENME KULLANILARAK KUMAŞ YUMUŞAKLIĞI VE BONCUKLANMA DEĞERLERİNİN OBJEKTİF BİR ŞEKİLDE ÖLÇÜLMESİ VE SINIFLANDIRILMASI

Fabric softness is a complex tactile sensation perceived by the user even before the fabrics are worn. Softness is usually the property of surface perceived by touching or pressing a finger on the fabric surface. Fabric friction properties significantly affect the tactile sensation of the garments. The yarn used, the finishing works, and the fabric structure (weaving, knitting, etc.) affect the softness. In addition, the hardness of the water used during washing, washing movements, the amount and content of the detergent and softener used also have permanent effects on the fabric softness. Softness can be evaluated by the jury members with proven effectiveness according to the predetermined scale. Our achievement within the scope of the thesis is to eliminate the differences that may occur as a result of the subjective evaluation, which may arise from qualitative observations by basing the degree of softness evaluated qualitatively on numerical data and to obtain clearer and more precise results by adding quantitative features to the evaluation process. The methodology developed for softness assessment is also applied for another textile deterioration parameter, namely pilling, and its results are also reported.Kumaş yumuşaklığı kumaşların giyilmesinden bile önce kullanıcı tarafından algılanan karmaşık bir dokunma hissidir. Yumuşaklık genellikle kumaşın parmaklarla sıkılması veya preslenmesi ile algılanan yüzey özelliğidir. Kumaş sürtünme özellikleri, giysilerin dokunma duyumlarını büyük ölçüde etkiler. Kullanılan iplik, bitim işleri ve kumaş yapısı (dokuma, örme vb.) yumuşaklığı etkilemektedir. Bunun yanında yıkama sırasında işlem gördüğü su sertliği, yıkama hareketleri, kullanılan deterjan ve yumuşatıcının miktarı ve içeriğinden de etkilenmektedir. Görsel olarak test edilen bir diğer tekstil özelliklerinden olan yumuşaklık, etkinliği kanıtlanmış jüri üyeleri tarafından aşağıdaki skalaya göre değerlendirilebilmektedir. Tez kapsamındaki kazanımımız nitel olarak değerlendirilen yumuşaklık derecesinin, sayısal verilere dayandırılarak, nitel gözlemlerden doğabilecek görsel değerlendirme sonucu oluşacak farklılıkların giderilmesi ve değerlendirme prosesine nicel özellik kazandırarak daha net ve kesin sonuçların elde edilmesidir. Yumuşaklık için geliştirilen metodoloji değerlendirme aynı zamanda başka bir tekstil bozulma parametresi, boncuklanma için de uygulanmış ve sonuçları raporlanmıştır.M.S. - Master of Scienc