138 citations · 265 across the 17 of their papers we have counts for
4 papers · 1 filter
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…
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…
Online Updating of Word Representations for Part-of-Speech Tagging
Wenpeng Yin, Tobias Schnabel, Hinrich Schütze
We propose online unsupervised domain adaptation (DA), which is performed incrementally as data comes in and is applicable when batch DA is not possible. In a part-of-speech (POS)…
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…