  One of the challenging aspects of applying machine learning is the need to
identify the algorithms that will perform best for a given dataset. This
process can be difficult, time consuming and often requires a great deal of
domain knowledge. We present Sommelier, an expert system for recommending the
machine learning algorithms that should be applied on a previously unseen
dataset. Sommelier is based on word embedding representations of the domain
knowledge extracted from a large corpus of academic publications. When
presented with a new dataset and its problem description, Sommelier leverages a
recommendation model trained on the word embedding representation to provide a
ranked list of the most relevant algorithms to be used on the dataset. We
demonstrate Sommelier's effectiveness by conducting an extensive evaluation on
121 publicly available datasets and 53 classification algorithms. The top
algorithms recommended for each dataset by Sommelier were able to achieve on
average 97.7% of the optimal accuracy of all surveyed algorithms.
