Simulations in Recommender Systems: An industry perspective
This work addresses the need for faster RS development in commercial contexts, but it is incremental as it synthesizes existing literature and principles without introducing new methods or data.
The paper tackles the challenge of constructing effective Recommender Systems (RS) by analyzing how simulations can accelerate iterative development processes, focusing on RS Simulation Platforms to identify strengths, gaps, and design principles for maximizing velocity.
The construction of effective Recommender Systems (RS) is a complex process, mainly due to the nature of RSs which involves large scale software-systems and human interactions. Iterative development processes require deep understanding of a current baseline as well as the ability to estimate the impact of changes in multiple variables of interest. Simulations are well suited to address both challenges and potentially leading to a high velocity construction process, a fundamental requirement in commercial contexts. Recently, there has been significant interest in RS Simulation Platforms, which allow RS developers to easily craft simulated environments where their systems can be analysed. In this work we discuss how simulations help to increase velocity, we look at the literature around RS Simulation Platforms, analyse strengths and gaps and distill a set of guiding principles for the design of RS Simulation Platforms that we believe will maximize the velocity of iterative RS construction processes.