Diet2Vec: Multi-scale analysis of massive dietary data
This work addresses the challenge of understanding eating habits for obesity research, though it is incremental as it applies existing embedding techniques to a new domain.
The paper tackled the problem of analyzing massive dietary data from smartphone apps to gain insights into obesity and weight loss, resulting in diet2vec, a model that learns embeddings for users, foods, and meals from a dataset of 55K users, producing interpretable representations.
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.