138 citations · 494 across the 31 of their papers we have counts for
5 papers · 1 filter
Large-scale Validation of Counterfactual Learning Methods: A Test-Bed
Damien Lefortier, Adith Swaminathan, Xiaotao Gu +2
The ability to perform effective off-policy learning would revolutionize the process of building better interactive systems, such as search engines and recommendation systems for e…
Unbiased Learning-to-Rank with Biased Feedback
Thorsten Joachims, Adith Swaminathan, Tobias Schnabel
Implicit feedback (e.g., clicks, dwell times, etc.) is an abundant source of data in human-interactive systems. While implicit feedback has many advantages (e.g., it is inexpensive…
Off-policy evaluation for slate recommendation
Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal +4
This paper studies the evaluation of policies that recommend an ordered set of items (e.g., a ranking) based on some context---a common scenario in web search, ads, and recommendat…
Unbiased Comparative Evaluation of Ranking Functions
Tobias Schnabel, Adith Swaminathan, Peter Frazier +1
Eliciting relevance judgments for ranking evaluation is labor-intensive and costly, motivating careful selection of which documents to judge. Unlike traditional approaches that mak…
Recommendations as Treatments: Debiasing Learning and Evaluation
Tobias Schnabel, Adith Swaminathan, Ashudeep Singh +2
Most data for evaluating and training recommender systems is subject to selection biases, either through self-selection by the users or through the actions of the recommendation sy…