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Counterfactual Data Augmentation for Debiased Coupon Recommendations Based on Potential Knowledge

Published: 13 May 2024 Publication History

Abstract

In real-world coupon recommendations, the coupon allocation process is influenced by both the recommendation model trained with historical interaction data and marketing tactics aimed at specific commercial goals. These tactics can cause an imbalance in user-coupon interactions, leading to a deviation from users' natural preferences. We refer to this deviation as the matching bias. Theoretically, unbiased data which is assumed to be collected via a randomized allocating policy (i.e., without model or tactics intervention) is ideal training data because it reflects the user's natural preferences. However, obtaining unbiased data in real-world scenarios is costly and sometimes unfeasible.
To address this problem, we propose a novel model-agnostic training paradigm named <u>C</u>ounterfactual <u>D</u>ata <u>A</u>ugmentation for debiased coupon recommendations based on <u>P</u>otential <u>K</u>nowledge (CDAPK) for the marketing scenario that allocates coupons with discounts. We leverage the counterfactual data augmentation technique to answer the following key question: If a user is offered a coupon that he has never seen before in his history, will he use this coupon? By creating the counterfactual interaction data and assigning labels based on the potential knowledge of the given scenario, CDAPK shifts the original data distribution into an unbiased distribution, facilitating model optimization and debiasing. The advantage of CDAPK lies in its ability to approximate the ideal states of the training data without depleting the real-world traffic flow. We implement CDAPK on five representative models: FM, DNN, NCF, MASKNET, and DEEPFM, and conduct extensive offline and online experiments against SOTA debiasing methods to validate the superiority of CDAPK.

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cover image ACM Conferences
WWW '24: Companion Proceedings of the ACM Web Conference 2024
May 2024
1928 pages
ISBN:9798400701726
DOI:10.1145/3589335
This work is licensed under a Creative Commons Attribution International 4.0 License.

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Published: 13 May 2024

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  1. counterfactual
  2. debias recommendation
  3. potential knowledge

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WWW '24
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WWW '24: The ACM Web Conference 2024
May 13 - 17, 2024
Singapore, Singapore

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