Meta Learning for User Cold Start Recommendation
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.
2019 International Joint Conference on Neural Networks (IJCNN)