Counterfactual Risk Minimization: Learning from Logged Bandit Feedback
This addresses the challenge of learning from limited feedback in online systems, such as ad placement and recommendation, by providing a principled approach that is incremental over existing propensity scoring methods.
The paper tackles the problem of batch learning from logged bandit feedback in online systems like ad placement, using counterfactual risk minimization to address the feedback's counterfactual nature and proposing the POEM algorithm for structured output prediction. It shows that POEM achieves substantially improved robustness and generalization performance on multi-label classification tasks compared to state-of-the-art methods.
We develop a learning principle and an efficient algorithm for batch learning from logged bandit feedback. This learning setting is ubiquitous in online systems (e.g., ad placement, web search, recommendation), where an algorithm makes a prediction (e.g., ad ranking) for a given input (e.g., query) and observes bandit feedback (e.g., user clicks on presented ads). We first address the counterfactual nature of the learning problem through propensity scoring. Next, we prove generalization error bounds that account for the variance of the propensity-weighted empirical risk estimator. These constructive bounds give rise to the Counterfactual Risk Minimization (CRM) principle. We show how CRM can be used to derive a new learning method -- called Policy Optimizer for Exponential Models (POEM) -- for learning stochastic linear rules for structured output prediction. We present a decomposition of the POEM objective that enables efficient stochastic gradient optimization. POEM is evaluated on several multi-label classification problems showing substantially improved robustness and generalization performance compared to the state-of-the-art.