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Unbiased Offline Evaluation of Contextual-bandit-based News Article Recommendation Algorithms
Contextual bandit algorithms have become popular for online recommendation
systems such as Digg, Yahoo! Buzz, and news recommendation in general.
\emph{Offline} evaluation of the effectiveness of new algorithms in these
applications is critical for protecting online user experiences but very
challenging due to their "partial-label" nature. Common practice is to create a
simulator which simulates the online environment for the problem at hand and
then run an algorithm against this simulator. However, creating simulator
itself is often difficult and modeling bias is usually unavoidably introduced.
In this paper, we introduce a \emph{replay} methodology for contextual bandit
algorithm evaluation. Different from simulator-based approaches, our method is
completely data-driven and very easy to adapt to different applications. More
importantly, our method can provide provably unbiased evaluations. Our
empirical results on a large-scale news article recommendation dataset
collected from Yahoo! Front Page conform well with our theoretical results.
Furthermore, comparisons between our offline replay and online bucket
evaluation of several contextual bandit algorithms show accuracy and
effectiveness of our offline evaluation method.Comment: 10 pages, 7 figures, revised from the published version at the WSDM
2011 conferenc
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