BanditRank: Learning to Rank Using Contextual Bandits

Phanideep Gampa, Sumio Fujita

Abstract: 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 used by gradient descent algorithms without approximation. BanditRank bridges this gap by directly optimizing a task specific measure, such as mean average precision (MAP). Specifically, a contextual bandit whose action is to rank input documents is trained using a policy gradient algorithm to directly maximize a 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 listwise approaches. To evaluate the effectiveness of BanditRank by answering five research questions, we conducted a series of experiments on datasets related to three different tasks, i.e., non-factoid, and factoid question answering and web search. We found that BanditRank performed better than strong baseline methods in respective tasks.