Last year has witnessed the re-flourishment of tag-aware recommender systems
supported by the LLM-enriched tags. Unfortunately, though large efforts have
been made, current solutions may fail to describe the diversity and uncertainty
inherent in user preferences with only tag-driven profiles. Recently, with the
development of geometry-based techniques, e.g., box embedding, diversity of
user preferences now could be fully modeled as the range within a box in high
dimension space. However, defect still exists as these approaches are incapable
of capturing high-order neighbor signals, i.e., semantic-rich multi-hop
relations within the user-tag-item tripartite graph, which severely limits the
effectiveness of user modeling. To deal with this challenge, in this paper, we
propose a novel algorithm, called BoxGNN, to perform the message aggregation
via combination of logical operations, thereby incorporating high-order
signals. Specifically, we first embed users, items, and tags as hyper-boxes
rather than simple points in the representation space, and define two logical
operations to facilitate the subsequent process. Next, we perform the message
aggregation mechanism via the combination of logical operations, to obtain the
corresponding high-order box representations. Finally, we adopt a volume-based
learning objective with Gumbel smoothing techniques to refine the
representation of boxes. Extensive experiments on two publicly available
datasets and one LLM-enhanced e-commerce dataset have validated the superiority
of BoxGNN compared with various state-of-the-art baselines. The code is
released online

提出了一种新的算法，称为 BoxGNN，通过组合逻辑操作来执行消息聚合，从而融合高阶信号，并通过基于体积的学习目标和 Gumbel 平滑技术来优化盒子的表示。在两个公开数据集和一个 LLM 增强的电子商务数据集上的大量实验验证了 BoxGNN 相对于各种最先进的基线方法的优越性。