Meta-Learning for User Cold-Start Recommendation

TL;DR: This work designs a recommendation framework that is trained to be reasonably good enough for a wide range of users and handles the user cold-start model much better than state-of-the art benchmark recommender systems.

Abstract: Recent studies in recommender systems emphasize the importance of dealing with the cold-start problem i.e. the modeling of new users or items in the recommendation system. Meta-learning approaches have gained popularity recently in the Machine Learning (ML) community for learning representations useful for a wide-range of tasks. Inspired by the generalizable modeling prowess of Model-Agnostic Meta Learning, we design a recommendation framework that is trained to be reasonably good enough for a wide range of users. During testing, to adapt to a specific user, the model parameters are updated by a few gradient steps. We evaluate our approach on three different benchmark datasets, from Movielens, Netflix, and MyFitnessPal. Through detailed simulation studies, we show that this framework handles the user cold-start model much better than state-of-the art benchmark recommender systems. We also show that the proposed approach performs well on the task of general recommendations to non cold-start users and effectively takes care of routine and eclectic preference trends of users.