Industrial recommender systems usually consist of the retrieval stage and the ranking stage, to handle the billion-scale of users and items. The retrieval stage retrieves candidate items relevant to user interests for recommendations and has attracted much attention. Frequently, a user shows refined multi-interests in a hierarchical structure. For example, a user likes Conan and Kuroba Kaito, which are the roles in hierarchical structure "Animation, Japanese Animation, Detective Conan". However, most existing methods ignore this hierarchical nature, and simply average the fine-grained interest information. Therefore, we propose a novel two-stage approach to explicitly modeling refined multi-interest in a hierarchical structure for recommendation. In the first hierarchical multi-interest mining stage, the hierarchical clustering and transformer-based model adaptively generate circles or sub-circles that users are interested in. In the second stage, the partition of retrieval space allows the EBR models to deal only with items within each circle and accurately capture users' refined interests. Experimental results show that the proposed approach achieves state-of-the-art performance. Our framework has also been deployed at Lofter.

翻译：工业推荐系统通常由检索阶段和排序阶段组成，以应对十亿级规模的用户与物品。检索阶段负责获取与用户兴趣相关的候选物品，近年来受到广泛关注。用户往往在分层结构中表现出精细化的多兴趣特征。例如，用户可能喜欢柯南和黑羽快斗，这两个角色位于"动画、日本动画、名侦探柯南"的分层结构中。然而，现有方法大多忽略这种分层特性，仅对细粒度兴趣信息进行简单平均。为此，我们提出一种新颖的两阶段方法，显式建模推荐场景中分层结构的精细化多兴趣。在第一阶段，即分层多兴趣挖掘阶段，基于层次聚类与Transformer的模型可自适应生成用户感兴趣的兴趣圈层或子圈层。在第二阶段，检索空间划分使得嵌入检索模型仅需处理每个圈层内的物品，从而准确捕捉用户的精细化兴趣。实验结果表明，该方法取得了最先进的性能。该框架已在Lofter平台部署。