RankFromSets: Scalable Set Recommendation with Optimal Recall

Jaan Altosaar, Rajesh Ranganath, Wesley Tansey

Abstract:

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
(https://github.com/altosaar/rankfromsets).