Using clickstream data to analyze online purchase intentions

Hoje em dia as técnicas de negócio tradicionais estão ultrapassadas devido à emergência de novos modelos de negócio, nomeadamente no espaço online através da Internet. Este novo espaço de comércio eletrónico difere substancialmente das atividades tradicionais que têm por bases espaços físicos. Assim, torna-se imperativo que as empresas adotem novas estratégias e sejam capazes de compreender as motivações que guiam os compradores online, caso pretendam suceder no competitivo ecossistema virtual.Os logs dos servidores são a principal fonte de informação, sobre os seus utilizadores, que as empresas dispõem. Estes ficheiros contêm detalhes sobre como cada cliente navegou pela loja eletrónica, mais ainda, através destes dados é possível reconstruir a sequência exata das páginas que cada um acedeu. Este tipo de dados, conhecidos como dados de clickstream, são fundamentais para conseguir compreender o comportamento dos utilizadores. Aliás, a análise e exploração desta informação são essenciais para melhorar a relação com os clientes.A análise de dados clickstream permite, acima de tudo, a compreensão de determindas intenções que motivam os utilizadores a realizar determinadas ações. A percentagem de conversão de utilizadores é uma das métricas mais conhecidas e que se relaciona diretamente com as intenções dos mesmos. Durante esta dissertação nós investigamos outro tipo de intenções, nomeadamente, fatores relacionados com os utilizadores que passam a ser compradores e ainda com a probabilidade de compra em tempo real. São utilizados dados concretos, provenientes de uma das maiores empresas europeias na área do retalho alimentar, para alimentar e avaliar diferentes modelos de data mining.Nowadays, traditional business techniques are almost deprecated due to the insurgence of the world of online virtual shopping, the so-called e-commerce. This new, in many ways, uncharted territory poses difficult challenges when it comes to apply marketing techniques especially traditional methods, as these are not effective when dealing with online customers. In this context, it is imperative that companies have a complete in-depth understanding of online behavior in order to succeed within this complex environment in which they compete.The server Web logs of each customer are the main sources of potentially useful information for online stores. These logs contain details on how each customer visited the online store, moreover, it is possible to reconstruct the sequence of accessed pages, the so-called clickstream data. This data is fundamental in depicting each customer's behavior. Analyzing and exploring this behavior is key to improve customer relationship management. The analysis of clickstream data allows for the understanding of customer intentions. One of the most studied measures regards customer conversion, that is, the percentage of customers that will actually perform a purchase during a specific online session. During this dissertation we investigate other relevant intentions, namely, customer purchasing engagement and real-time purchase likelihood. Actual data from a major European online grocery retail store will be used to support and evaluate different data mining models

Insights on Assistive Orientation and Mobility of People with Visual Impairment Based on Large-Scale Longitudinal Data

Assistive applications for orientation and mobility promote independence for people with visual impairment (PVI). While typical design and evaluation of such applications involves small-sample iterative studies, we analyze large-scale longitudinal data from a geographically diverse population. Our publicly released dataset from iMove, a mobile app supporting orientation of PVI, contains millions of interactions by thousands of users over a year. Our analysis (i) examines common functionalities, settings, assistive features, and movement modalities in iMove dataset and (ii) discovers user communities based on interaction patterns. We find that the most popular interaction mode is passive, where users receive more notifications, often verbose, while in motion and perform fewer actions. The use of built-in assistive features such as enlarged text indicate a high presence of users with residual sight. Users fall into three distinct groups: (C1) users interested in surrounding points of interest, (C2) users interacting in short bursts to inquire about current location, and (C3) users with long active sessions while in motion. iMove was designed with C3 in mind, and one strength of our contribution is providing meaningful semantics for unanticipated groups, C1 and C2. Our analysis reveals insights that can be generalized to other assistive orientation and mobility applications

User Acquisition and Engagement in Digital News Media

Generating revenue has been a major issue for the news industry and journalism over the past decade. In fact, vast availability of free online news sources causes online news media agencies to face user acquisition and engagement as pressing issues more than before. Although digital news media agencies are seeking sustainable relationships with their users, their current business models do not satisfy this demand. As a matter of fact, they need to understand and predict how much an article can engage a reader as a crucial step in attracting readers, and then maximize the engagement using some strategies. Moreover, news media companies need effective algorithmic tools to identify users who are prone to subscription. Last but not least, online news agencies need to make smarter decisions in the way that they deliver articles to users to maximize the potential benefits. In this dissertation, we take the first steps towards achieving these goals and investigate these challenges from data mining /machine learning perspectives. First, we investigate the problem of understanding and predicting article engagement in terms of dwell time as one of the most important factors in digital news media. In particular, we design data exploratory models studying the textual elements (e.g., events, emotions) involved in article stories, and find their relationships with the engagement patterns. In the prediction task, we design a framework to predict the article dwell time based on a deep neural network architecture which exploits the interactions among important elements (i.e., augmented features) in the article content as well as the neural representation of the content to achieve the better performance. In the second part of the dissertation, we address the problem of identifying valuable visitors who are likely to subscribe in the future. We suggest that the decision for subscription is not a sudden, instantaneous action, but it is the informed decision based on positive experience with the newspaper. As such, we propose effective engagement measures and show that they are effective in building the predictive model for subscription. We design a model that predicts not only the potential subscribers but also the time that a user would subscribe. In the last part of this thesis, we consider the paywall problem in online newspapers. The traditional paywall method offers a non-subscribed reader a fixed number of free articles in a period of time (e.g., a month), and then directs the user to the subscription page for further reading. We argue that there is no direct relationship between the number of paywalls presented to readers and the number of subscriptions, and that this artificial barrier, if not used well, may disengage potential subscribers and thus may not well serve its purpose of increasing revenue. We propose an adaptive paywall mechanism to balance the benefit of showing an article against that of displaying the paywall (i.e., terminating the session). We first define the notion of cost and utility that are used to define an objective function for optimal paywall decision making. Then, we model the problem as a stochastic sequential decision process. Finally, we propose an efficient policy function for paywall decision making. All the proposed models are evaluated on real datasets from The Globe and Mail which is a major newspaper in Canada. However, the proposed techniques are not limited to any particular dataset or strict requirement. Alternatively, they are designed based on the datasets and settings which are available and common to most of newspapers. Therefore, the models are general and can be applied by any online newspaper to improve user engagement and acquisition

The Utilization of Data Analysis Techniques in Predicting Student Performance in Massive Open Online Courses (MOOCs)

The growth of the Internet has enabled the popularity of open online learning platforms to increase over the years. This has led to the inception of Massive Open Online Courses (MOOCs) that enrol, millions of people, from all over the world. Such courses operate under the concept of open learning, where content does not have to be delivered via standard mechanisms that institutions employ, such as physically attending lectures. Instead learning occurs online via recorded lecture material and online tasks. This shift has allowed more people to gain access to education, regardless of their learning background. However, despite these advancements in delivering education, completion rates for MOOCs are low. In order to investigate this issue, the paper explores the impact that technology has on open learning and identifies how data about student performance can be captured to predict trend so that at risk students can be identified before they drop-out. In achieving this, subjects surrounding student engagement and performance in MOOCs and data analysis techniques are explored to investigate how technology can be used to address this issue. The paper is then concluded with our approach of predicting behaviour and a case study of the eRegister system, which has been developed to capture and analyse data. Keywords: Open Learning; Prediction; Data Mining; Educational Systems; Massive Open Online Course; Data Analysi

Predicting Paid Certification in Massive Open Online Courses

Massive open online courses (MOOCs) have been proliferating because of the free or low-cost offering of content for learners, attracting the attention of many stakeholders across the entire educational landscape. Since 2012, coined as “the Year of the MOOCs”, several platforms have gathered millions of learners in just a decade. Nevertheless, the certification rate of both free and paid courses has been low, and only about 4.5–13% and 1–3%, respectively, of the total number of enrolled learners obtain a certificate at the end of their courses. Still, most research concentrates on completion, ignoring the certification problem, and especially its financial aspects. Thus, the research described in the present thesis aimed to investigate paid certification in MOOCs, for the first time, in a comprehensive way, and as early as the first week of the course, by exploring its various levels. First, the latent correlation between learner activities and their paid certification decisions was examined by (1) statistically comparing the activities of non-paying learners with course purchasers and (2) predicting paid certification using different machine learning (ML) techniques. Our temporal (weekly) analysis showed statistical significance at various levels when comparing the activities of non-paying learners with those of the certificate purchasers across the five courses analysed. Furthermore, we used the learner’s activities (number of step accesses, attempts, correct and wrong answers, and time spent on learning steps) to build our paid certification predictor, which achieved promising balanced accuracies (BAs), ranging from 0.77 to 0.95. Having employed simple predictions based on a few clickstream variables, we then analysed more in-depth what other information can be extracted from MOOC interaction (namely discussion forums) for paid certification prediction. However, to better explore the learners’ discussion forums, we built, as an original contribution, MOOCSent, a cross- platform review-based sentiment classifier, using over 1.2 million MOOC sentiment-labelled reviews. MOOCSent addresses various limitations of the current sentiment classifiers including (1) using one single source of data (previous literature on sentiment classification in MOOCs was based on single platforms only, and hence less generalisable, with relatively low number of instances compared to our obtained dataset;) (2) lower model outputs, where most of the current models are based on 2-polar iii iv classifier (positive or negative only); (3) disregarding important sentiment indicators, such as emojis and emoticons, during text embedding; and (4) reporting average performance metrics only, preventing the evaluation of model performance at the level of class (sentiment). Finally, and with the help of MOOCSent, we used the learners’ discussion forums to predict paid certification after annotating learners’ comments and replies with the sentiment using MOOCSent. This multi-input model contains raw data (learner textual inputs), sentiment classification generated by MOOCSent, computed features (number of likes received for each textual input), and several features extracted from the texts (character counts, word counts, and part of speech (POS) tags for each textual instance). This experiment adopted various deep predictive approaches – specifically that allow multi-input architecture - to early (i.e., weekly) investigate if data obtained from MOOC learners’ interaction in discussion forums can predict learners’ purchase decisions (certification). Considering the staggeringly low rate of paid certification in MOOCs, this present thesis contributes to the knowledge and field of MOOC learner analytics with predicting paid certification, for the first time, at such a comprehensive (with data from over 200 thousand learners from 5 different discipline courses), actionable (analysing learners decision from the first week of the course) and longitudinal (with 23 runs from 2013 to 2017) scale. The present thesis contributes with (1) investigating various conventional and deep ML approaches for predicting paid certification in MOOCs using learner clickstreams (Chapter 5) and course discussion forums (Chapter 7), (2) building the largest MOOC sentiment classifier (MOOCSent) based on learners’ reviews of the courses from the leading MOOC platforms, namely Coursera, FutureLearn and Udemy, and handles emojis and emoticons using dedicated lexicons that contain over three thousand corresponding explanatory words/phrases, (3) proposing and developing, for the first time, multi-input model for predicting certification based on the data from discussion forums which synchronously processes the textual (comments and replies) and numerical (number of likes posted and received, sentiments) data from the forums, adapting the suitable classifier for each type of data as explained in detail in Chapter 7

Human exploration of complex knowledge spaces

Driven by need or curiosity, as humans we constantly act as information seekers. Whenever we work, study, play, we naturally look for information in spaces where pieces of our knowledge and culture are linked through semantic and logic relations. Nowadays, far from being just an abstraction, these information spaces are complex structures widespread and easily accessible via techno-systems: from the whole World Wide Web to the paramount example of Wikipedia. They are all information networks. How we move on these networks and how our learning experience could be made more efficient while exploring them are the key questions investigated in the present thesis. To this end concepts, tools and models from graph theory and complex systems analysis are borrowed to combine empirical observations of real behaviours of users in knowledge spaces with some theoretical findings of cognitive science research. It is investigated how the knowledge space structure can affect its own exploration in learning-type tasks, and how users do typically explore the information networks, when looking for information or following some learning paths. The research approach followed is exploratory and moves along three main lines of research. Enlarging a previous work in algorithmic education, the first contribution focuses on the topological properties of the information network and how they affect the \emph{efficiency} of a simulated learning exploration. To this end a general class of algorithms is introduced that, standing on well-established findings on educational scheduling, captures some of the behaviours of an individual moving in a knowledge space while learning. In exploring this space, learners move along connections, periodically revisiting some concepts, and sometimes jumping on very distant ones. To investigate the effect of networked information structures on the dynamics, both synthetic and real-world graphs are considered, such as subsections of Wikipedia and word-association graphs. The existence is revealed of optimal topological structures for the defined learning dynamics. They feature small-world and scale-free properties with a balance between the number of hubs and of the least connected items. Surprisingly the real-world networks analysed turn out to be close to optimality. To uncover the role of semantic content of the bit of information to be learned in a information-seeking tasks, empirical data on user traffic logs in the Wikipedia system are then considered. From these, and by means of first-order Markov chain models, some users paths over the encyclopaedia can be simulated and treated as proxies for the real paths. They are then analysed in an abstract semantic level, by mapping the individual pages into points of a semantic reduced space. Recurrent patterns along the walks emerge, even more evident when contrasted with paths originated in information-seeking goal oriented games, thus providing some hints about the unconstrained navigation of users while seeking for information. Still, different systems need to be considered to evaluate longer and more constrained and structured learning dynamics. This is the focus of the third line of investigation, in which learning paths are extracted from advances scientific textbooks and treated as they were walks suggested by their authors throughout an underlying knowledge space. Strategies to extract the paths from the textbooks are proposed, and some preliminary results are discussed on their statistical properties. Moreover, by taking advantages of the Wikipedia information network, the Kauffman theory of adjacent possible is formalized in a learning context, thus introducing the adjacent learnable to refer to the part of the knowledge space explorable by the reader as she learns new concepts by following the suggested learning path. Along this perspective, the paths are analysed as particular realizations of the knowledge space explorations, thus allowing to quantitatively contrast different approaches to education

5th International Conference on Advanced Research Methods and Analytics (CARMA 2023)

Research methods in economics and social sciences are evolving with the increasing availability of Internet and Big Data sources of information. As these sources, methods, and applications become more interdisciplinary, the 5th International Conference on Advanced Research Methods and Analytics (CARMA) is a forum for researchers and practitioners to exchange ideas and advances on how emerging research methods and sources are applied to different fields of social sciences as well as to discuss current and future challenges.Martínez Torres, MDR.; Toral Marín, S. (2023). 5th International Conference on Advanced Research Methods and Analytics (CARMA 2023). Editorial Universitat Politècnica de València. https://doi.org/10.4995/CARMA2023.2023.1700

Multimodal Approach for Big Data Analytics and Applications

The thesis presents multimodal conceptual frameworks and their applications in improving the robustness and the performance of big data analytics through cross-modal interaction or integration. A joint interpretation of several knowledge renderings such as stream, batch, linguistics, visuals and metadata creates a unified view that can provide a more accurate and holistic approach to data analytics compared to a single standalone knowledge base. Novel approaches in the thesis involve integrating multimodal framework with state-of-the-art computational models for big data, cloud computing, natural language processing, image processing, video processing, and contextual metadata. The integration of these disparate fields has the potential to improve computational tools and techniques dramatically. Thus, the contributions place multimodality at the forefront of big data analytics; the research aims at mapping and under- standing multimodal correspondence between different modalities. The primary contribution of the thesis is the Multimodal Analytics Framework (MAF), a collaborative ensemble framework for stream and batch processing along with cues from multiple input modalities like language, visuals and metadata to combine benefits from both low-latency and high-throughput. The framework is a five-step process: Data ingestion. As a first step towards Big Data analytics, a high velocity, fault-tolerant streaming data acquisition pipeline is proposed through a distributed big data setup, followed by mining and searching patterns in it while data is still in transit. The data ingestion methods are demonstrated using Hadoop ecosystem tools like Kafka and Flume as sample implementations. Decision making on the ingested data to use the best-fit tools and methods. In Big Data Analytics, the primary challenges often remain in processing heterogeneous data pools with a one-method-fits all approach. The research introduces a decision-making system to select the best-fit solutions for the incoming data stream. This is the second step towards building a data processing pipeline presented in the thesis. The decision-making system introduces a Fuzzy Graph-based method to provide real-time and offline decision-making. Lifelong incremental machine learning. In the third step, the thesis describes a Lifelong Learning model at the processing layer of the analytical pipeline, following the data acquisition and decision making at step two for downstream processing. Lifelong learning iteratively increments the training model using a proposed Multi-agent Lambda Architecture (MALA), a collaborative ensemble architecture between the stream and batch data. As part of the proposed MAF, MALA is one of the primary contributions of the research.The work introduces a general-purpose and comprehensive approach in hybrid learning of batch and stream processing to achieve lifelong learning objectives. Improving machine learning results through ensemble learning. As an extension of the Lifelong Learning model, the thesis proposes a boosting based Ensemble method as the fourth step of the framework, improving lifelong learning results by reducing the learning error in each iteration of a streaming window. The strategy is to incrementally boost the learning accuracy on each iterating mini-batch, enabling the model to accumulate knowledge faster. The base learners adapt more quickly in smaller intervals of a sliding window, improving the machine learning accuracy rate by countering the concept drift. Cross-modal integration between text, image, video and metadata for more comprehensive data coverage than a text-only dataset. The final contribution of this thesis is a new multimodal method where three different modalities: text, visuals (image and video) and metadata, are intertwined along with real-time and batch data for more comprehensive input data coverage than text-only data. The model is validated through a detailed case study on the contemporary and relevant topic of the COVID-19 pandemic. While the remainder of the thesis deals with text-only input, the COVID-19 dataset analyzes both textual and visual information in integration. Post completion of this research work, as an extension to the current framework, multimodal machine learning is investigated as a future research direction