From Theory to Behaviour: Towards a General Model of Engagement

Engagement is a fuzzy concept. In the present work we operationalize engagement mechanistically by linking it directly to human behaviour and show that the construct of engagement can be used for shaping and interpreting data-driven methods. First we outline a formal framework for engagement modelling. Second we expanded on our previous work on theory-inspired data-driven approaches to better model the engagement process by proposing a new modelling technique, the Melchoir Model. Third, we illustrate how, through model comparison and inspection, we can link machine-learned models and underlying theoretical frameworks. Finally we discuss our results in light of a theory-driven hypothesis and highlight potential application of our work in industry.Comment: In review for being included in the proceedings of "Conference on Games

A SLR on Customer Dropout Prediction

Dropout prediction is a problem that is being addressed with machine learning algorithms; thus, appropriate approaches to address the dropout rate are needed. The selection of an algorithm to predict the dropout rate is only one problem to be addressed. Other aspects should also be considered, such as which features should be selected and how to measure accuracy while considering whether the features are appropriate according to the business context in which they are employed. To solve these questions, the goal of this paper is to develop a systematic literature review to evaluate the development of existing studies and to predict the dropout rate in contractual settings using machine learning to identify current trends and research opportunities. The results of this study identify trends in the use of machine learning algorithms in different business areas and in the adoption of machine learning algorithms, including which metrics are being adopted and what features are being applied. Finally, some research opportunities and gaps that could be explored in future research are presented.info:eu-repo/semantics/publishedVersio

A SLR on Customer Dropout Prediction

Dropout prediction is a problem that is being addressed with machine learning algorithms; thus, appropriate approaches to address the dropout rate are needed. The selection of an algorithm to predict the dropout rate is only one problem to be addressed. Other aspects should also be considered, such as which features should be selected and how to measure accuracy while considering whether the features are appropriate according to the business context in which they are employed. To solve these questions, the goal of this paper is to develop a systematic literature review to evaluate the development of existing studies and to predict the dropout rate in contractual settings using machine learning to identify current trends and research opportunities. The results of this study identify trends in the use of machine learning algorithms in different business areas and in the adoption of machine learning algorithms, including which metrics are being adopted and what features are being applied. Finally, some research opportunities and gaps that could be explored in future research are presented.info:eu-repo/semantics/publishedVersio

Optimality, Scalability, and Reneging in Bandit Learning

Bandit learning has been widely applied to handle the exploration-exploitation dilemma in sequential decision problems. To solve the dilemma, a large number of bandit algorithms have been proposed. While many of these algorithms have been proved to be order-optimal with respect to regret, the difference between the best expected reward and that actually achieved, there remain two fundamental challenges. First, the “efficiency” of the best-performing bandit algorithms is often unsatisfactory, where the efficiency is measured jointly with respect to the performance in maximizing rewards as well as the computational complexity. For instance, the Information Directed Sampling (IDS), variance-based IDS (VIDS), and Kullback-Leibler Upper Confidence Bounds (KL-UCB) have often been reported to achieve outstanding performance with respect to regret. Unfortunately, they suffer from high computational complexity even after approximation, and exhibit poor scalability of computational complexity as the number of arms increases. Second, most of the existing bandit algorithms assume that the sequential decision-making process will continue forever without an end. However, users may renege and stop playing. They also assume the underlying reward distribution is homoscedastic. Both these assumptions are often violated in real-world applications, where participants may disengage from future interactions if they do not have a rewarding experience, and at the same time, the variances of underlying distributions differs under different contexts. To address the aforementioned challenges, we propose a family of novel bandit algorithms. To address the efficiency issue, we propose Biased Maximum Likelihood Estimation (BMLE) - a family of novel bandit algorithms that generally apply to both parametric and non-parametric reward distributions, often have a closed-form solution and low computation complexity, have a quantifiable regret bound, and demonstrate satisfactory empirical performance. To enable bandit algorithms handle the reneging risk and reward heteroscedasticity, we propose a Heteroscedastic Reneging Upper Confidence Bound policy (HR-UCB) - a novel UCB-type algorithm that achieves outstanding and quantifiable performance in the presence of reneging risk and heteroscedasticity