Unbiased Offline Evaluation of Contextual-bandit-based News Article Recommendation Algorithms

TL;DR

This paper introduces a data-driven, unbiased offline evaluation method for contextual bandit algorithms in news recommendation, avoiding simulator biases and aligning well with online results.

Contribution

The paper presents a novel replay methodology for unbiased offline evaluation of contextual bandit algorithms, which is simple, adaptable, and provably accurate.

Findings

Empirical results match theoretical predictions.

Offline replay evaluation aligns with online performance.

Method outperforms traditional simulator-based evaluations.

Abstract

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.