In recent years, the recommendation content on e-commerce platforms has become increasingly rich -- a single user feed may contain multiple entities, such as selling products, short videos, and content posts. To deal with the multi-entity recommendation problem, an intuitive solution is to adopt the shared-network-based architecture for joint training. The idea is to transfer the extracted knowledge from one type of entity (source entity) to another (target entity). However, different from the conventional same-entity cross-domain recommendation, multi-entity knowledge transfer encounters several important issues: (1) data distributions of the source entity and target entity are naturally different, making the shared-network-based joint training susceptible to the negative transfer issue, (2) more importantly, the corresponding feature schema of each entity is not exactly aligned (e.g., price is an essential feature for selling product while missing for content posts), making the existing methods no longer appropriate. Recent researchers have also experimented with the pre-training and fine-tuning paradigm. Again, they only consider the scenarios with the same entity type and feature systems, which is inappropriate in our case. To this end, we design a pre-training & fine-tuning based Multi-entity Knowledge Transfer framework called MKT. MKT utilizes a multi-entity pre-training module to extract transferable knowledge across different entities. In particular, a feature alignment module is first applied to scale and align different feature schemas. Afterward, a couple of knowledge extractors are employed to extract the common and entity-specific knowledge. In the end, the extracted common knowledge is adopted for target entity model training. Through extensive offline and online experiments, we demonstrated the superiority of MKT over multiple State-Of-The-Art methods.

翻译：近年来，电商平台的推荐内容日益丰富——单一用户信息流中可能包含多种实体，例如商品销售、短视频和内容帖子。为解决多实体推荐问题，一种直观方案是采用基于共享网络的架构进行联合训练，其核心思想是将从源实体提取的知识迁移至目标实体。然而，与传统同实体跨域推荐不同，多实体知识迁移存在以下关键问题：（1）源实体与目标实体的数据分布天然存在差异，使得基于共享网络的联合训练易受负迁移问题影响；（2）更重要的是，各实体的特征模式无法完全对齐（例如，价格是商品销售的核心特征，但在内容帖子中缺失），导致现有方法不再适用。近期研究也尝试了预训练-微调范式，但同样仅考虑相同实体类型与特征体系的场景，不适用于本问题。为此，我们提出基于预训练与微调的多实体知识迁移框架MKT。该框架通过多实体预训练模块提取跨实体的可迁移知识：首先利用特征对齐模块对异构特征模式进行尺度缩放与对齐，随后采用成对知识提取器抽取出共性知识与实体特异性知识，最终将提取的共性知识用于目标实体模型训练。通过大量离线与在线实验，我们证明了MKT相较于多个最先进方法的优越性。