Knowledge graphs (KGs) have become vitally important in modern recommender systems, effectively improving performance and interpretability. Fundamentally, recommender systems aim to identify user interests based on historical interactions and recommend suitable items. However, existing works overlook two key challenges: (1) an interest corresponds to a potentially large set of related items, and (2) the lack of explicit, fine-grained exploitation of KG information and interest connectivity. This leads to an inability to reflect distinctions between entities and interests when modeling them in a single way. Additionally, the granularity of concepts in the knowledge graphs used for recommendations tends to be coarse, failing to match the fine-grained nature of user interests. This homogenization limits the precise exploitation of knowledge graph data and interest connectivity. To address these limitations, we introduce a novel embedding-based model called InBox. Specifically, various knowledge graph entities and relations are embedded as points or boxes, while user interests are modeled as boxes encompassing interaction history. Representing interests as boxes enables containing collections of item points related to that interest. We further propose that an interest comprises diverse basic concepts, and box intersection naturally supports concept combination. Across three training steps, InBox significantly outperforms state-of-the-art methods like HAKG and KGIN on recommendation tasks. Further analysis provides meaningful insights into the variable value of different KG data for recommendations. In summary, InBox advances recommender systems through box-based interest and concept modeling for sophisticated knowledge graph exploitation.

翻译：知识图谱（KG）在现代推荐系统中已变得至关重要，有效提升了模型的性能与可解释性。从根本上讲，推荐系统旨在基于历史交互识别用户兴趣，并推荐合适的物品。然而，现有工作忽视了两个关键挑战：（1）一个兴趣可能对应大量相关物品，以及（2）缺乏对知识图谱信息与兴趣连通性的显式细粒度挖掘。这导致在单一方式建模实体与兴趣时，无法体现两者间的区别。此外，推荐所用知识图谱中的概念粒度往往较为粗糙，难以匹配用户兴趣的细粒度特性。这种同质化限制了知识图谱数据与兴趣连通性的精准利用。为应对这些局限，我们提出了一种新颖的嵌入模型InBox。具体而言，各类知识图谱实体与关系被嵌入为点或盒，而用户兴趣则被建模为包含交互历史的盒。将兴趣表示为盒，使其能够包含与该兴趣相关的物品点集合。我们进一步提出，兴趣由多样化的基本概念构成，而盒的交集运算能够自然支持概念组合。通过三个训练步骤，InBox在推荐任务上显著优于HAKG和KGIN等先进方法。进一步的分析为不同知识图谱数据在推荐中的差异化价值提供了有意义的见解。总之，InBox通过基于盒的兴趣与概念建模，推进了知识图谱的精细挖掘在推荐系统中的应用。