Cross-Domain Recommendation Systems Enhanced by Large Language Model-Based Semantic Memory and Query Expansion
Keywords:
cross-domain recommendation, large language models, semantic memory, query expansion, system architecture, fairness, deployment infrastructureAbstract
The proliferation of heterogeneous digital platforms has intensified the need for recommendation systems that transcend the boundaries of individual domains, providing coherent and personalized experiences across disparate content repositories. This paper examines the integration of large language model-based semantic memory and query expansion mechanisms as a transformative paradigm for cross-domain recommendation. In contrast to traditional collaborative filtering and matrix factorization approaches that struggle with domain-specific sparsity and structural mismatches, semantic memory constructs derived from pretrained language models enable the retention and transfer of user preferences and conceptual associations at a deeply semantic level, functioning as a robust bridge between uncorrelated item spaces. Meanwhile, contextual query expansion dynamically enriches sparse user interaction signals by generating relevant, domain-aware descriptors that improve intent representation and retrieval accuracy. We present a system-level architecture that fuses these two capabilities, emphasizing structural trade-offs in latency, memory footprint, modularity, and governance. The discussion further addresses the inherent challenges of maintaining fairness, privacy, and transparency in systems that rely on large-scale pretrained models, proposing practical frameworks for responsible deployment. Through a comprehensive analysis of deployment infrastructures, data governance regimes, and evaluation methodologies, we argue that the proposed integration offers a sustainable and adaptable pathway for next-generation recommender ecosystems, while also highlighting the policy implications of leveraging generative model capacities in user-facing socio-technical infrastructures.
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