Exploratory Methods for Evaluating Recommender Systems

A common and recently widely accepted problem in the field of machine learning is the black box nature of many algorithms. In practice, many machine learning algorithms can only be viewed and evaluated in terms of their inputs and outputs, without taking their internal workings into account. Perhaps the most notorious examples in this context are artificial neural networks and deep learning techniques, but they are certainly not the only techniques that suffer from this problem. Matrix factorisation models for recommendation systems, for example, suffer from the same lack of interpretability. Our research focuses on applying and adapting pattern mining techniques to gain meaningful insights in recommendation algorithms by analysing them in terms of both their input and output, also allowing us to compare different algorithms and discover the hidden biases that lead to those differences.