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BanditRank: Learning to Rank Using Contextual Bandits

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Advances in Knowledge Discovery and Data Mining (PAKDD 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12714))

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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.

P. Gampa—Work conducted while the first author was in research internship at Yahoo! JAPAN Research.

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Notes

  1. 1.

    Permutation \({}_{n}\mathrm {P}_{r}\) is an increasing function of r.

  2. 2.

    We also plan to release the code used for experiments post the publication of paper.

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Author information

Authors and Affiliations

  1. Indian Institute of Technology (BHU) Varanasi, Varanasi, India

    Phanideep Gampa

  2. Yahoo Japan Corporation, Tokyo, Japan

    Sumio Fujita

Authors
  1. Phanideep Gampa
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  2. Sumio Fujita
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Corresponding author

Correspondence to Phanideep Gampa .

Editor information

Editors and Affiliations

  1. IIIT, Hyderabad, Hyderabad, India

    Kamal Karlapalem

  2. Chinese University of Hong Kong, Shatin, Hong Kong

    Hong Cheng

  3. Virginia Tech, Arlington, VA, USA

    Naren Ramakrishnan

  4. Jawaharlal Nehru University, New Delhi, India

    R. K. Agrawal

  5. IIIT Hyderabad, Hyderabad, India

    P. Krishna Reddy

  6. University of Minnesota, Minneapolis, MN, USA

    Jaideep Srivastava

  7. IIIT Delhi, New Delhi, India

    Tanmoy Chakraborty

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Gampa, P., Fujita, S. (2021). BanditRank: Learning to Rank Using Contextual Bandits. In: Karlapalem, K., et al. Advances in Knowledge Discovery and Data Mining. PAKDD 2021. Lecture Notes in Computer Science(), vol 12714. Springer, Cham. https://doi.org/10.1007/978-3-030-75768-7_21

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