  We propose an extensible deep learning method that uses reinforcement
learning to train neural networks for offline ranking in information retrieval
(IR). We call our method BanditRank as it treats ranking as a contextual bandit
problem. In the domain of learning to rank for IR, current deep learning models
are trained on objective functions different from the measures they are
evaluated on. Since most evaluation measures are discrete quantities, they
cannot be leveraged by directly using gradient descent algorithms without an
approximation. BanditRank bridges this gap by directly optimizing a
task-specific measure, such as mean average precision (MAP), using gradient
descent. Specifically, a contextual bandit whose action is to rank input
documents is trained using a policy gradient algorithm to directly maximize the
reward. The reward can be a single measure, such as MAP, or a combination of
several measures. The notion of ranking is also inherent in BanditRank, similar
to the current \textit{listwise} approaches. To evaluate the effectiveness of
BanditRank, we conducted a series of experiments on datasets related to three
different tasks, i.e., web search, community, and factoid question answering.
We found that it performs better than state-of-the-art methods when applied on
the question answering datasets. On the web search dataset, we found that
BanditRank performed better than four strong listwise baselines including
LambdaMART, AdaRank, ListNet and Coordinate Ascent.
