  With information systems becoming larger scale, recommendation systems are a
topic of growing interest in machine learning research and industry. Even
though progress on improving model design has been rapid in research, we argue
that many advances fail to translate into practice because of two limiting
assumptions. First, most approaches focus on a transductive learning setting
which cannot handle unseen users or items and second, many existing methods are
developed for static settings that cannot incorporate new data as it becomes
available. We argue that these are largely impractical assumptions on
real-world platforms where new user interactions happen in real time. In this
survey paper, we formalize both concepts and contextualize recommender systems
work from the last six years. We then discuss why and how future work should
move towards inductive learning and incremental updates for recommendation
model design and evaluation. In addition, we present best practices and
fundamental open challenges for future research.
