All Times EDT
Keywords: set-valued recommendation, food recommender systems
We study a variant of user-item recommendation where each item has a set of attributes, such as tags on an image, user reactions to a post, or foods in a meal. We focus on the latter example, with the goal of building a meal recommender for a diet tracking app. Meal recommendation is challenging: (i) each item (meal) is rarely logged by more than a handful of users, (ii) the database of attributes (foods) is large, and (iii) each item is tagged with only a handful of attributes. We propose RankFromSets (RFS), a flexible and scalable class of models for recommending items with attributes. RFS treats item attributes as set-valued side information and learns embeddings to discriminate items a user will consume from items a user is unlikely to consume. We develop theory connecting the RFS objective to optimal recall and show that the learnable class of models for RFS is a superset of several previously-proposed models. We then develop a stochastic optimization method for RFS that uses negative sampling to scale to massive problems like meal recommendation. In experiments on a real dataset of 55k users logging 16M meals, the new method outperforms competing approaches while learning embeddings that reveal interpretable structure in user behavior. Code is available on GitHub at https://github.com/altosaar/rankfromsets.