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.

本文提出了一种名为 SAR-Net 的场景感知排名网络，它通过学习用户的跨场景兴趣，以应对不同场景下的个性化推荐问题。借助注意力模块、线性转换层、去偏置专家网络等组件，SAR-Net 自适应地提取场景特定特征，并通过公平系数的使用，以增强对数据的公平性和因人工干预而产生偏差的恢复能力。实验证明了其在超过 8000 万个用户和 155 万个旅行产品的离线数据集和在线 A/B 测试中的优异表现。