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构成，以应对十亿级用户和物品的规模。检索阶段负责召回与用户兴趣相关的候选物品用于推荐，因而受到广泛关注。用户经常在分层结构中表现出精细的多兴趣特征。例如，某用户喜欢柯南和黑羽快斗，这些角色位于"动漫、日本动漫、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，仅简单平均细粒度的兴趣信息。为此，我们提出了一种新颖的两阶段方法，显式地在分层结构中建模精细多兴趣用于推荐。在第一阶段的分层多兴趣挖掘中，基于分层聚类和Transformer的模型自适应生成用户感兴趣的兴趣圈或子圈。第二阶段通过划分检索空间，使EBR模型仅需处理各兴趣圈内的物品，精准捕捉用户的精细兴趣。实验结果表明，所提方法达到了最先进水平。该框架已在Lofter平台部署。