Online travel platforms (OTPs), e.g., Ctrip.com or Fliggy.com, can effectively provide travel-related products or services to users. In this paper, we focus on the multi-scenario click-through rate (CTR) prediction, i.e., training a unified model to serve all scenarios. Existing multi-scenario based CTR methods struggle in the context of OTP setting due to the ignorance of the cold-start users who have very limited data. To fill this gap, we propose a novel method named Cold-Start based Multi-scenario Network (CSMN). Specifically, it consists of two basic components including: 1) User Interest Projection Network (UIPN), which firstly purifies users' behaviors by eliminating the scenario-irrelevant information in behaviors with respect to the visiting scenario, followed by obtaining users' scenario-specific interests by summarizing the purified behaviors with respect to the target item via an attention mechanism; and 2) User Representation Memory Network (URMN), which benefits cold-start users from users with rich behaviors through a memory read and write mechanism. CSMN seamlessly integrates both components in an end-to-end learning framework. Extensive experiments on real-world offline dataset and online A/B test demonstrate the superiority of CSMN over state-of-the-art methods.

翻译：在线旅游平台（OTPs），例如携程网或飞猪网，能够有效地向用户提供旅游相关产品或服务。本文聚焦于多场景点击率（CTR）预测，即训练一个统一模型服务于所有场景。现有基于多场景的CTR方法在OTP环境下难以发挥作用，因为它们忽视了数据量极少的冷启动用户。为填补这一空白，我们提出了一种名为冷启动多场景网络（CSMN）的新方法。具体而言，该方法包含两个基本组件：1）用户兴趣投影网络（UIPN），该网络首先通过消除用户行为中与当前访问场景无关的信息来净化用户行为，然后通过注意力机制汇总针对目标项目的净化行为，从而获得用户的场景特定兴趣；2）用户表示记忆网络（URMN），该网络通过记忆读写机制，使冷启动用户能够受益于具有丰富行为的用户。CSMN将这两个组件无缝整合在一个端到端学习框架中。在真实离线数据集和在线A/B测试上的大量实验表明，CSMN相比现有最先进方法具有优越性。