In multi-behavior recommendation, auxiliary behaviors such as clicks, add-to-cart, and purchases can provide richer supervisory information for predicting target behaviors. Although existing graph and hypergraph methods are capable of modeling high-order relationships among users, items, and behaviors, they still have limitations in heterogeneous semantics, user-specific weighting, and sequence dependency modeling. While standard Transformers excel at sequence modeling, their shared feedforward mapping struggles to accommodate the differentiated requirements of heterogeneous latent patterns in multi-behavior scenarios. To address this, this paper proposes the Personalized Hypergraph-enhanced Kolmogorov-Arnold Network Transformer (PHKT). Specifically, we design a personalized dynamic hypergraph module that performs behavior-aware weighting of item similarities based on users' historical behavior sequences to capture user-specific heterogeneous high-order relationships. Meanwhile, a Transformer is used as the temporal backbone to model the evolution of short- and long-term preferences, and KAN is introduced to replace the traditional MLP in the feedforward network to enhance fine-grained modeling capability for nonlinear responses to different latent patterns. Experiments on three real datasets, Tmall, RetailRocket, and IJCAI, show that PHKT consistently outperforms nine strong baseline models across multiple evaluation metrics, demonstrating its effectiveness in multi-behavior preference modeling and target behavior prediction.

翻译：摘要：在多行为推荐中，点击、加入购物车和购买等辅助行为能够为目标行为预测提供更丰富的监督信息。尽管现有的图和超图方法能够建模用户、物品与行为间的高阶关系，但在异质语义、用户特定权重以及序列依赖建模方面仍存在局限性。虽然标准Transformer在序列建模方面表现优异，但其共享的前馈映射难以适应多行为场景中异质潜在模式的差异化需求。为此，本文提出个性化超图增强型Kolmogorov-Arnold网络Transformer（PHKT）。具体而言，我们设计了一个个性化动态超图模块，该模块基于用户历史行为序列对物品相似度进行行为感知加权，以捕获用户特定的异质高阶关系；同时，以Transformer作为时序骨干模型来建模短期与长期偏好的演化，并引入KAN替代前馈网络中的传统MLP，以增强对非线性响应不同潜在模式的细粒度建模能力。在Tmall、RetailRocket和IJCAI三个真实数据集上的实验表明，PHKT在多项评估指标上持续优于九个强基线模型，验证了其在多行为偏好建模与目标行为预测中的有效性。