Conventional approaches to recommendation often do not explicitly,take into account information on previously shown recommendations and their recorded responses. One reason is that, since we do,not know the outcome of actions the system did not take, learning directly from such logs is not a straightforward task. Several,methods for off-policy or counterfactual learning have been proposed in recent years, but their efficacy for the recommendation,task remains understudied. Due to the limitations of offline datasets,and the lack of access of most academic researchers to online experiments, this is a non-trivial task. Simulation environments can,provide a reproducible solution to this problem.,In this work, we conduct the first broad empirical study of counterfactual learning methods for recommendation, in a simulated,environment. We consider various different policy-based methods,that make use of the Inverse Propensity Score (IPS) to perform,Counterfactual Risk Minimisation (CRM), as well as value-based,methods based on Maximum Likelihood Estimation (MLE). We highlight how existing off-policy learning methods fail due to stochastic,and sparse rewards, and show how a logarithmic variant of the,traditional IPS estimator can solve these issues, whilst convexifying,the objective and thus facilitating its optimisation. Additionally,,under certain assumptions the value- and policy-based methods,have an identical parameterisation, allowing us to propose a new,model that combines both the MLE and CRM objectives. Extensive,experiments show that this “Dual Bandit” approach achieves stateof-the-art performance in a wide range of scenarios, for varying,logging policies, action spaces and training sample sizes.
