Situating Recommender Systems in Practice: Towards Inductive Learning and Incremental Updates
This survey addresses the gap between research and industry in recommender systems by highlighting practical limitations and proposing directions for more applicable models.
The paper identifies that many recommender system advances fail in practice due to assumptions of transductive learning and static data, which are impractical for real-world platforms with unseen users/items and real-time interactions, and it surveys recent work to advocate for inductive learning and incremental updates.
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.