  Smart phone apps that enable users to easily track their diets have become
widespread in the last decade. This has created an opportunity to discover new
insights into obesity and weight loss by analyzing the eating habits of the
users of such apps. In this paper, we present diet2vec: an approach to modeling
latent structure in a massive database of electronic diet journals. Through an
iterative contract-and-expand process, our model learns real-valued embeddings
of users' diets, as well as embeddings for individual foods and meals. We
demonstrate the effectiveness of our approach on a real dataset of 55K users of
the popular diet-tracking app LoseIt\footnote{http://www.loseit.com/}. To the
best of our knowledge, this is the largest fine-grained diet tracking study in
the history of nutrition and obesity research. Our results suggest that
diet2vec finds interpretable results at all levels, discovering intuitive
representations of foods, meals, and diets.

Machine Learning;Machine Learning;Applications