SemaCDR: LLM-Powered Transferable Semantics for Cross-Domain Sequential Recommendation
Summary: arXiv:2604.09551v1 Announce Type: cross
Introduction
In the rapidly evolving landscape of recommendation systems, cross-domain recommendation (CDR) has emerged as a pivotal area of research. This approach primarily addresses the challenges of data sparsity and cold-start issues in target domains by leveraging information from data-rich source domains. Traditional CDR methods, however, often depend on domain-specific features or identifiers that fail to exhibit transferability across different domains. This limitation hinders their ability to effectively capture inter-domain semantic patterns. In response to these challenges, we introduce SemaCDR, a novel semantics-driven framework for cross-domain sequential recommendation.
Overview of SemaCDR
SemaCDR represents a significant advancement in the field of CDR by integrating large language models (LLMs) to construct a unified semantic space. The framework innovatively creates multiview item features that combine LLM-generated domain-agnostic semantics with domain-specific content. This integration is aligned through a mechanism known as contrastive regularization, allowing for enhanced coherence and meaning in the generated recommendations.
Key Components of SemaCDR
- LLM-Generated Semantics: SemaCDR systematically produces both domain-specific and domain-agnostic semantics using large language models, ensuring a comprehensive understanding of item features.
- Adaptive Fusion: The framework employs an adaptive fusion mechanism that synthesizes unified preference representations, optimizing the way user preferences are interpreted across various domains.
- Alignment of Behavior Sequences: By aligning cross-domain behavior sequences, SemaCDR effectively synthesizes interaction sequences from source, target, and mixed domains, enhancing the relevance of recommendations.
Experimental Results
To validate the effectiveness of SemaCDR, extensive experiments were conducted on real-world datasets. The results demonstrated that SemaCDR consistently outperforms state-of-the-art baselines, highlighting its capability in capturing coherent intra-domain patterns while facilitating knowledge transfer across domains. This performance is attributed to its unique approach in leveraging LLMs for semantic understanding and its robust adaptive fusion techniques.
Conclusion
As the demand for more sophisticated recommendation systems continues to grow, SemaCDR offers a promising solution to the inherent challenges of cross-domain recommendation. By harnessing the power of large language models and emphasizing the importance of semantics, SemaCDR not only enhances recommendation accuracy but also sets a new standard for future research in the field. The framework paves the way for more intelligent systems that can seamlessly adapt and transfer knowledge across various domains, ultimately enriching user experiences in diverse applications.