Recommender Systems (RS) are a fundamental part of any relevant e-commerce
website, and the inclusion, improvement and optimization of this kind of systems
is a growing trend. As these systems evolve, people are more demanding with the
accuracy and explainability of their recommendations.
The goal of this work is to create a RS that provides simple explanations of the
recommendations that it provides. These explanations are obtained by nding the
di erent relations that can be interpreted from the data of any dataset.
This work shows that recommendations can be explained in an easy way with
Machine Learning (ML) algorithms that let contexts be understood from di erent
perspectives. In this master thesis, every ML algorithm provides recommendations.
Since algorithms can be explained, recommendations provided by them can also
be explained in natural language, and, in some cases, the explanations are even
provided with a helper picture.
ML algorithms used in this master thesis provide recommendations with some features
that can be queried by the user in order to meet user's current recommendation
expectations or ltering settings. Users, therefore, obtain not only explainability,
but a very open recommender system where dark patterns do not exist and there are
neither boring nor repetitive recommendations. Users can easily explore explainable
recommendation spaces that are prepared speci cally for each one of them, with parameters
that help them decide implicitly the ratio of exploration and exploitation.
All this work is presented in a framework that allows to encode as much context
information as wanted by capturing as best recommendation space as possible at
the time explainability results evolve even richer.
This framework comprehends all the di erent representation, aggregation, computing
and visualization techniques coded in a modular and extendable way so that
it can be applied to any recommendation scenario.
A variety of datasets have been tested in many ways showing surprisingly positive
results of recommendation explanation capabilities in all the datasets.
