With the increase of content pages and interactive buttons 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 multiple scenarios given multiple 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-domain and multi-task recommendation. PEPNet takes personalized prior information as input and dynamically scales the bottom-level Embedding and top-level DNN hidden units through gate mechanisms. \textit{Embedding Personalized Network (EPNet)} performs personalized selection on Embedding to fuse features with different importance for different users in multiple domains. \textit{Parameter Personalized Network (PPNet)} executes personalized modification on DNN parameters to balance targets with different sparsity for different users in multiple tasks. We have made a series of special engineering optimizations combining the Kuaishou training framework and the online deployment environment. By infusing personalized selection of Embedding and personalized modification of DNN parameters, PEPNet tailored to the interests of each individual obtains significant performance gains, with online improvements exceeding 1\% in multiple task metrics across multiple domains. We have deployed PEPNet in Kuaishou apps, serving over 300 million users every day.

翻译：随着在线购物和视频网站等在线服务中内容页面和交互按钮的增加，工业级推荐系统面临多领域和多任务推荐的挑战。多任务与多领域推荐的核心在于，在多种用户行为场景下准确捕捉用户兴趣。本文提出一种即插即用的《参数与嵌入个性化网络》（PEPNet），用于多领域和多任务推荐。PEPNet以个性化先验信息为输入，通过门控机制动态缩放底层嵌入和高层深度神经网络隐藏单元。其中，嵌入个性化网络（EPNet）对嵌入进行个性化选择，融合不同领域用户间具有不同重要性的特征；参数个性化网络（PPNet）对深度神经网络参数进行个性化调整，平衡不同任务用户间具有不同稀疏性的目标。我们结合快手训练框架与在线部署环境进行了系列工程优化。通过注入个性化嵌入选择与个性化深度神经网络参数调整，PEPNet为每位用户量身定制兴趣表达，在多领域多项任务指标上均取得超过1%的线上性能提升。目前，PEPNet已在快手应用部署，每日服务超3亿用户。