Adobe Inc.
CONSTRAINT SAMPLING REINFORCEMENT LEARNING FOR RECOMMENDATION SYSTEMS

Abstract:

Systems and methods for sequential recommendation are described. Embodiments receive a user interaction history including interactions of a user with a plurality of items, select a constraint from a plurality of candidate constraints based on lifetime values observed for the candidate constraints, wherein the lifetime values are based on items predicted for other users using a recommendation network subject to the candidate constraints, and predict a next item for the user based on the user interaction history using the recommendation network subject to the selected constraint.