Cold-start recommendation is one of the major challenges faced by recommender systems (RS). Herein, we focus on the user cold-start problem. Recently, methods utilizing side information or meta-learning have been used to model cold-start users. However, it is difficult to deploy these methods to industrial RS. There has not been much research that pays attention to the user cold-start problem in the matching stage. In this paper, we propose Cold & Warm Net based on expert models who are responsible for modeling cold-start and warm-up users respectively. A gate network is applied to incorporate the results from two experts. Furthermore, dynamic knowledge distillation acting as a teacher selector is introduced to assist experts in better learning user representation. With comprehensive mutual information, features highly relevant to user behavior are selected for the bias net which explicitly models user behavior bias. Finally, we evaluate our Cold & Warm Net on public datasets in comparison to models commonly applied in the matching stage and it outperforms other models on all user types. The proposed model has also been deployed on an industrial short video platform and achieves a significant increase in app dwell time and user retention rate.

我们提出了基于专家模型的冷启动与热身网络，专家模型分别负责冷启动和热身用户建模，门控网络用于整合两个专家的结果，引入动态知识蒸馏作为教师选择器，以帮助专家更好地学习用户表示，通过综合的互信息选择与用户行为高度相关的特征用于显式建模用户行为偏差，最终在公共数据集上评估了我们的冷启动与热身网络，在匹配阶段常用模型的比较中表现优异，对于所有用户类型，我们的模型胜过其他模型，还将该模型应用于一个工业级短视频平台，在应用停留时间和用户留存率方面实现了显著的增加。

冷热净: 解决推荐系统中的冷启动用户问题