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，电子商务平台上的推荐内容日益丰富——单个用户信息流可能包含多种实体，例如销售商品、短视频和内容帖子。为应对多实体推荐问题，一种直观的解决方案是采用基于共享网络的架构进行联合训练，其核心思想是将从一种实体（源实体）提取的知识迁移至另一种实体（目标实体）。然而，与传统的同实体跨领域推荐不同，多实体知识迁移面临若干关键问题：（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容易受到负迁移问题的影响；（2）更重要的是，不同实体对应的特征体系并不完全对齐（例如价格是销售商品的关键特征，但在内容帖子中却不存在），导致现有方法不再适用。近期研究也尝试了预训练与微调范式，但这些工作仅考虑实体类型和特征体系相同的场景，不适用于本文的情况。为此，我们设计了一个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不同实体的可迁移知识：首先采用特征对齐模块对不同特征体系进行缩放与对齐；随后使用多个知识提取器分别提取通用知识与实体特定知识；最终将提取的通用知识用于目标实体模型训练。通过大量离线与在线实验，我们验证了MKT相较于多种前沿方法的优越性。