SARDINE: A Simulator for Automated Recommendation in Dynamic and Interactive Environments

TL;DR

SARDINE is a flexible, interpretable simulator designed to model complex, dynamic, and interactive recommendation environments, facilitating research and development of advanced recommender systems beyond traditional accuracy metrics.

Contribution

The paper introduces SARDINE, a novel simulator that captures the complexity of interactive recommendation environments, supporting research in data-driven and reinforcement learning methods.

Findings

Studied existing recommendation methods using SARDINE's environments.

Uncovered new insights about the behavior of recommendation algorithms.

Demonstrated the simulator's flexibility across nine diverse scenarios.

Abstract

Simulators can provide valuable insights for researchers and practitioners who wish to improve recommender systems, because they allow one to easily tweak the experimental setup in which recommender systems operate, and as a result lower the cost of identifying general trends and uncovering novel findings about the candidate methods. A key requirement to enable this accelerated improvement cycle is that the simulator is able to span the various sources of complexity that can be found in the real recommendation environment that it simulates. With the emergence of interactive and data-driven methods - e.g., reinforcement learning or online and counterfactual learning-to-rank - that aim to achieve user-related goals beyond the traditional accuracy-centric objectives, adequate simulators are needed. In particular, such simulators must model the various mechanisms that render the recommendation environment dynamic and interactive, e.g., the effect of recommendations on the user or the effect of biased data on subsequent iterations of the recommender system. We therefore propose SARDINE, a flexible and interpretable recommendation simulator that can help accelerate research in interactive and data-driven recommender systems. We demonstrate its usefulness by studying existing methods within nine diverse environments derived from SARDINE, and even uncover novel insights about them.