SARDINE: A Simulator for Automated Recommendation in Dynamic and Interactive Environments
arxiv(2023)
摘要
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.
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