Self Hyper-parameter Tuning for Stream Recommendation Algorithms

E-commerce platforms explore the interaction between users and digital content – user generated streams of events – to build and maintain dynamic user preference models which are used to make meaningful recommendations. However, the accuracy of these incremental models is critically affected by the choice of hyper-parameters. So far, the incremental recommendation algorithms used to process data streams rely on human expertise for hyper-parameter tuning. In this work we apply our Self Hyper-Parameter Tuning (SPT) algorithm to incremental recommendation algorithms. SPT adapts the Melder-Mead optimisation algorithm to perform hyper-parameter tuning. First, it creates three models with random hyper-parameter values and, then, at dynamic size intervals, assesses and applies the Melder-Mead operators to update their hyper-parameters until the models converge. The main contribution of this work is the adaptation of the SPT method to incremental matrix factorisation recommendation algorithms. The proposed method was evaluated with well-known recommendation data sets. The results show that SPT systematically improves data stream recommendations.info:eu-repo/semantics/publishedVersio

One-Class Classification: Taxonomy of Study and Review of Techniques

One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/novelty detection and concept learning. In this paper we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure

Transformer approaches on hyper-parameter optimization and anomaly detection with applications in stream tuning

Hyper-parameter Optimisation consists of finding the parameters that maximise a model’s performance. However, this mainly concerns processes in which the model shouldn’t change over time. Hence, how should an online model be optimised? For this, we pose the following research question: How and when should the model be optimised? For the optimisation part, we explore the transformer architecture as a function mapping data statistics into model parameters, by means of graph attention layers, together with reinforcement learning approaches, achieving state of the art results. On the other hand, in order to detect when the model should be optimised, we use the transformer architecture to empower already existing anomaly detection methods, in this case, the Variational Auto Encoder. Finally, we join these developed methods in a framework capable of deciding when an optimisation should take part and how to do it, aiding the stream tuning process; Sumário: Abordagens de Transformer em Optimização de Hiper-Parâmetros e Deteção de Anomalias com Aplicações em Stream Tuning Optimização de hiper parâmetros consiste em encontrar os parâmetros que maximizam a performance de um modelo. Contudo, maioritariamente, isto diz respeito a processos em que o modelo não muda ao longo do tempo. Assim, como deve um modelo online ser optimizado? Para este fim, colocamos a seguinte pergunta: Como e quando deve ser o modelo optimizado? Para a fase de optimização, exploramos a arquitectura de transformador, como uma função que mapeia estatísticas sobre dados para parâmetros de modelos, utilizando atenção de grafos junto de abordagens de aprendizagem por reforço, alcançando resultados de estado da arte. Por outro lado, para detectar quando o modelo deve ser optimizado, utilizamos a arquitectura de transformador, reforçando abordagens de detecção de anomalias já existentes, o Variational Auto Encoder. Finalmente, juntamos os métodos desenvolvidos numa framework capaz de decidir quando se deve realizar uma optimização e como o fazer, auxiliando o processo de tuning em stream

AutoML: state of the art with a focus on anomaly detection, challenges, and research directions

International audienceThe last decade has witnessed the explosion of machine learning research studies with the inception of several algorithms proposed and successfully adopted in different application domains. However, the performance of multiple machine learning algorithms is very sensitive to multiple ingredients (e.g., hyper-parameters tuning and data cleaning) where a significant human effort is required to achieve good results. Thus, building well-performing machine learning algorithms requires domain knowledge and highly specialized data scientists. Automated machine learning (autoML) aims to make easier and more accessible the use of machine learning algorithms for researchers with varying levels of expertise. Besides, research effort to date has mainly been devoted to autoML for supervised learning, and only a few research proposals have been provided for the unsupervised learning. In this paper, we present an overview of the autoML field with a particular emphasis on the automated methods and strategies that have been proposed for unsupervised anomaly detection