Cold & Warm Net: Addressing Cold-Start Users in Recommender Systems
Xiangyu Zhang Zongqiang Kuang Zehao Zhang Fan Huang Xianfeng Tan
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.