تقرير
Cold-Start based Multi-Scenario Ranking Model for Click-Through Rate Prediction
| العنوان: | Cold-Start based Multi-Scenario Ranking Model for Click-Through Rate Prediction |
|---|---|
| المؤلفون: | Chen, Peilin, Wen, Hong, Zhang, Jing, Lv, Fuyu, Li, Zhao, Shen, Qijie, Tao, Wanjie, Zhou, Ying, Zhang, Chao |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Information Retrieval |
| الوصف: | 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. Comment: accepted by DASFAA'23 as a Research Paper |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2304.07858 |
| رقم الأكسشن: | edsarx.2304.07858 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |