Simulations in Recommender Systems: An industry perspective

TL;DR

This paper discusses how simulation platforms can accelerate the development and testing of recommender systems by providing controlled environments, analyzing current tools, and proposing design principles to enhance iterative development.

Contribution

It offers a comprehensive analysis of RS simulation platforms, identifies their strengths and gaps, and proposes guiding principles for designing platforms that boost development velocity.

Findings

Simulations increase the speed of RS development.

Current platforms have notable strengths and limitations.

Guiding principles can improve RS simulation platform design.

Abstract

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.