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：在线旅游平台可以为用户提供有效的旅游相关产品或服务。本文关注多场景点击率（CTR）预测，即训练一个统一模型为所有场景提供服务。现有的多场景CTR方法在OTP设置下存在困难，因为它们忽略了冷启动用户，这些用户具有非常有限的数据。为了填补这一空白，我们提出了一种新的方法，称为冷启动基础的多场景网络（CSMN）。具体地，它由两个基本组件组成，包括：1）用户兴趣投影网络（UIPN），该网络通过根据访问场景消除与场景无关信息来净化用户行为，随后通过注意机制总结与目标项相关的净化行为以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来受益于具有丰富行为的用户，帮助冷启动用户。CSMN将这两个组件无缝地集成在一个端到端的学习框架中。实验结果表明，CSMN优于现有的 state-of-the-art 方法，经过实际的离线数据集和在线A/B测试。
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