PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information

With the increase of content pages and display styles in online services such as online-shopping and video-watching websites, industrial-scale recommender systems face challenges in multi-domain and multi-task recommendations. The core of multi-task and multi-domain recommendation is to accurately capture user interests in different domains given different user behaviors. In this paper, we propose a plug-and-play \textit{\textbf{P}arameter and \textbf{E}mbedding \textbf{P}ersonalized \textbf{Net}work (\textbf{PEPNet})} for multi-task recommendation in the multi-domain setting. PEPNet takes features with strong biases as input and dynamically scales the bottom-layer embeddings and the top-layer DNN hidden units in the model through a gate mechanism. By mapping personalized priors to scaling weights ranging from 0 to 2, PEPNet introduces both parameter personalization and embedding personalization. Embedding Personalized Network (EPNet) selects and aligns embeddings with different semantics under multiple domains. Parameter Personalized Network (PPNet) influences DNN parameters to balance interdependent targets in multiple tasks. We have made a series of special engineering optimizations combining the Kuaishou training framework and the online deployment environment. We have deployed the model in Kuaishou apps, serving over 300 million daily users. Both online and offline experiments have demonstrated substantial improvements in multiple metrics. In particular, we have seen a more than 1\% online increase in three major scenarios.