Abstract: Sequential recommendation effectively captures the dynamic evolution of user interests. However, systems relying on single-domain data often face challenges like data sparsity and recommendation homogeneity. Cross-domain sequential recommendation was proposed to address these issues by integrating user behavior sequences from multiple domains, which alleviates data sparsity and enables a more comprehensive modeling of user interest dynamics. However, existing methods often employ a uniform global strategy when fusing cross-domain interaction information, neglecting the diversity and complexity of user interests. Moreover, simple graph structures are insufficient to capture complex high-order interaction features between users and items, resulting in incomplete representation of cross-domain interaction information. To address these issues, this paper proposes an interest-enhanced cross-domain sequential recommendation model based on graph and hypergraph fusion. To tackle the problem of insufficient mining of deep-seated user preferences, a capsule network structure was introduced in the private domain. Its dynamic routing mechanism adaptively aggregated contextual information from item embeddings in sequences, extracting multiple potential user interest points to supplement single-domain user profiling. In the shared domain, a hybrid architecture combining Graph Neural Networks and Hypergraph Neural Networks was proposed to overcome the limitations of traditional graph structures in capturing complex group associations and higher-order interactions. This design enabled comprehensive capture of user preference features across different dimensions through multi-level feature interactions, enhancing the representational capacity for cross-domain behavioral dependencies. Subsequently, the user's unique preferences and general preferences were deeply integrated through a sequence relation learning module and a contrastive learning module, generating a complete user preference embedding. Experimental validation on the Hvideo and Amazon datasets showed that compared to the strongest baseline models, the proposed HGIE-CDSR model achieved average improvements of 4.95% and 8.39% in MRR, and 3.58% and 14.37% in NDCG, respectively. Ablation study results further verified the effectiveness of each module within the model.

摘要： 序列推荐能捕捉用户兴趣的动态变化，但单领域的序列推荐系统面临着数据稀疏性和推荐同质性等问题。跨域序列推荐系统通过整合多领域的用户行为序列信息，缓解了数据稀疏问题并全面建模用户兴趣的动态演变过程。然而，现有方法在融合跨域交互信息时多采用统一的全局策略，忽略了用户兴趣的多样性和复杂性，且简单图结构难以捕捉用户和项目之间复杂的高阶交互特征，导致跨领域交互信息表征不够全面。针对上述问题，本文提出一种基于图与超图融合的兴趣增强跨域序列推荐模型。在专有域中，针对用户深层次偏好信息挖掘不足的问题，引入胶囊网络结构，通过动态路由机制自适应聚合序列中项目嵌入的上下文信息，提取用户多个潜在兴趣点，作为对单域用户偏好的补充；在共享域中，针对传统图结构难以表达群体间复杂关联和高阶交互特性的局限，提出融合图神经网络与超图神经网络的混合架构。通过多层次特征交互来全面捕捉用户不同维度的偏好特征，增强跨域行为依赖关系的表示能力。最终，经过序列关系学习模块和对比学习模块后，将用户的特有偏好和通用偏好进行深度融合，生成完整的用户偏好嵌入。在数据集Hvideo和Amazon上进行实验验证表明，与最优基线模型相比，所提模型的MRR指标平均提升4.95%和8.39%，NDCG指标平均提升3.58%和14.37%；消融实验结果进一步验证了模型中各个模块的有效性。