SAR-Net: A Scenario-Aware Ranking Network for PersonalizedFair Recommendation in Hundreds of Travel Scenarios

• URL: http://arxiv.org/abs/2110.06475v1
• Date: Wed, 13 Oct 2021 03:49:45 GMT
• Title: SAR-Net: A Scenario-Aware Ranking Network for PersonalizedFair Recommendation in Hundreds of Travel Scenarios
• Authors: Qijie Shen, Wanjie Tao, Jing Zhang, Hong Wen, Zulong Chen, Quan Lu
• Abstract summary: We propose a novel Scenario-Aware Ranking Network (SAR-Net) to address these issues. Experiments on an offline dataset covering over 80 million users and 1.55 million travel items and an online A/B test demonstrate the effectiveness of our SAR-Net.
• Score: 11.603889783538309
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The travel marketing platform of Alibaba serves an indispensable role for hundreds of different travel scenarios from Fliggy, Taobao, Alipay apps, etc. To provide personalized recommendation service for users visiting different scenarios, there are two critical issues to be carefully addressed. First, since the traffic characteristics of different scenarios, it is very challenging to train a unified model to serve all. Second, during the promotion period, the exposure of some specific items will be re-weighted due to manual intervention, resulting in biased logs, which will degrade the ranking model trained using these biased data. In this paper, we propose a novel Scenario-Aware Ranking Network (SAR-Net) to address these issues. SAR-Net harvests the abundant data from different scenarios by learning users' cross-scenario interests via two specific attention modules, which leverage the scenario features and item features to modulate the user behavior features, respectively. Then, taking the encoded features of previous module as input, a scenario-specific linear transformation layer is adopted to further extract scenario-specific features, followed by two groups of debias expert networks, i.e., scenario-specific experts and scenario-shared experts. They output intermediate results independently, which are further fused into the final result by a multi-scenario gating module. In addition, to mitigate the data fairness issue caused by manual intervention, we propose the concept of Fairness Coefficient (FC) to measures the importance of individual sample and use it to reweigh the prediction in the debias expert networks. Experiments on an offline dataset covering over 80 million users and 1.55 million travel items and an online A/B test demonstrate the effectiveness of our SAR-Net and its superiority over state-of-the-art methods.

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