  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.
