  It is said that we live in the age of data, and that data is ubiquitous and
readily available if one has the tools to harness it. That may well be true,
but so is the opposite. It is ever more common to try to start a data science
project only to find oneself without quality data. Be it due to just not having
collected the needed features, or due to insufficient data, or even legality
issues, the list goes on. When this happens, either the project is prematurely
abandoned, or similar datasets are searched for and used. However, finding a
dataset that answers your needs in terms of features, type of ratings, etc.,
may not be an easy task, this is particularly the case for recommender systems.
In this work, a methodology for the generation of synthetic datasets for
recommender systems is presented, thus allowing to overcome the obstacle of not
having quality data in sufficient amount readily available. With this
methodology, one can generate a synthetic dataset for recommendation composed
by numerical/ordinal and nominal features. The dataset is built with Gaussian
copulas, Dirichlet and Gaussian distributions, a Multinomial Logit model and a
Fuzzy Logic Inference System that generates the ratings according to different
user behavioural profiles and perceived item quality.
