  Recommender systems have become fundamental building blocks of modern online
products and services, and have a substantial impact on user experience. In the
past few years, deep learning methods have attracted a lot of research, and are
now heavily used in modern real-world recommender systems. Nevertheless,
dealing with recommendations in the cold-start setting, e.g., when a user has
done limited interactions in the system, is a problem that remains far from
solved. Meta-learning techniques, and in particular optimization-based
meta-learning, have recently become the most popular approaches in the academic
research literature for tackling the cold-start problem in deep learning models
for recommender systems. However, current meta-learning approaches are not
practical for real-world recommender systems, which have billions of users and
items, and strict latency requirements. In this paper we show that it is
possible to obtaining similar, or higher, performance on commonly used
benchmarks for the cold-start problem without using meta-learning techniques.
In more detail, we show that, when tuned correctly, standard and widely adopted
deep learning models perform just as well as newer meta-learning models. We
further show that an extremely simple modular approach using common
representation learning techniques, can perform comparably to meta-learning
techniques specifically designed for the cold-start setting while being much
more easily deployable in real-world applications.
