Counterfactual Evaluation and Learning from Logged User Feedback

Опубликовано 5 октября 2016, 18:36

Interactive systems like search engines, recommender systems, and ad placement platforms are ubiquitous. Evaluating and optimizing these systems is hard - users' feedback governs system performance, and gathering their feedback in repeated randomized experiments is costly. I study how we can use logs collected from deployed systems to perform offline evaluation and learning. I will outline two projects (Evaluation: Recommendations as Treatments, ICML'16 and Learning: Counterfactual Risk Minimization, ICML'15) that advance the state of the art for these problems. I will also briefly reference the tutorial Thorsten Joachims and I taught at SIGIR'16 (cs.cornell.edu/~adith/CfactSIG... that explores these counterfactual evaluation and learning problems in more detail.

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