With the increase of pages and buttons in real-world applications, industrial-scale recommender systems face multi-domain and multi-task challenges. On the one hand, users and items in multiple domains suffer inconsistent distributions. On the other hand, multiple tasks have distinctive sparsity and interdependence. Personalization modeling is the core of recommender systems. Accurate personalization estimation helps to capture the degree of user preference for items in different situations, especially in the case of multiple domains and multiple tasks. In multi-task and multi-domain recommendation, how to introduce personalized priors into the model in the right place and in the right way is crucial. In this paper, we propose a plug-and-play Parameter and Embedding Personalized Network (PEPNet) for multi-task recommendation in the multi-domain setting. PEPNet takes features with strong bias as input and dynamically acts on the bottom-layer embeddings or the top-layer DNN hidden units in the model through the gate mechanism. By mapping significant 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. To further adapt to the characteristics of the model, we have made corresponding engineering optimizations on the Embedding and DNN parameter update strategies. We have deployed the model in Kuaishou and Kuaishou Express 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 domains.

翻译：随着现实应用中页面和按钮数量的增加，工业级推荐系统面临多领域和多任务挑战。一方面，多领域中的用户和物品存在不一致的数据分布。另一方面，多任务具有独特的稀疏性和相互依赖性。个性化建模是推荐系统的核心。准确的个性化估计有助于捕捉用户在不同情境下对物品的偏好程度，尤其是在多领域和多任务场景中。在多任务和多领域推荐中，如何在正确的位置以正确的方式将个性化先验引入模型至关重要。本文提出了一种即插即用的参数与嵌入个性化网络（PEPNet），用于多领域设置下的多任务推荐。PEPNet将具有强偏置的特征作为输入，通过门控机制动态作用于模型的底层嵌入或顶层DNN隐藏单元。通过将显著先验映射至0到2范围内的缩放权重，PEPNet同时引入了参数个性化和嵌入个性化。嵌入个性化网络（EPNet）在多领域下选择并对齐具有不同语义的嵌入。参数个性化网络（PPNet）影响DNN参数以平衡多任务中相互依赖的目标。为进一步适应模型特性，我们对嵌入和DNN参数更新策略进行了相应的工程优化。该模型已部署于快手和快手极速版应用，服务超3亿日活跃用户。在线和离线实验均表明，模型在多个指标上取得了显著提升，尤其在三大主要领域中实现了超过1%的在线指标增长。