Enterprise AI Analysis: November 10-14, 2025
Enhancing Dual-Target Cross-Domain Recommendation via Similar User Bridging
This paper introduces SUBCDR, a novel framework leveraging Large Language Models (LLMs) to bridge similar users across domains, significantly enhancing dual-target cross-domain recommendation performance. It employs a Multi-Interests-Aware Prompt Learning mechanism for comprehensive user profile generation, disentangling domain-invariant interests and capturing fine-grained preferences. SUBCDR constructs intra-domain bipartite graphs and an inter-domain heterogeneous graph to link similar users. It uses GCNs for intra-domain modeling and an Inter-domain Hierarchical Attention Network (InterHAN) for knowledge transfer, learning both shared and specific user representations. Extensive experiments on seven public datasets demonstrate SUBCDR's superior performance over state-of-the-art methods, especially in low-overlap scenarios, making it highly effective for diverse real-world recommendation challenges.
Executive Impact & Key Findings
SUBCDR provides significant advancements in cross-domain recommendation, particularly in data-sparse environments. Here are the key performance indicators and operational benefits.
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Average HR@10 Improvement
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Average NDCG@10 Improvement
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Deep Analysis & Enterprise Applications
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Explores how Large Language Models (LLMs) are used to generate comprehensive, multi-interest user profiles, a novel approach for capturing nuanced preferences and facilitating cross-domain user similarity identification.
- Multi-Interests-Aware Prompt Learning: LLMs generate detailed user profiles, disentangling domain-invariant interests.
- Fine-grained Preferences: Captures subtle user preferences beyond surface-level interactions.
- Semantic Alignment: Enables more accurate identification of similar users across different domains.
Details the construction of two types of graphs: intra-domain bipartite graphs for user-item interactions and an inter-domain heterogeneous graph to link similar users based on shared interest patterns, crucial for robust knowledge transfer.
- Intra-domain Bipartite Graph: Models user-item interactions within each domain using GCNs.
- Inter-domain Heterogeneous Graph: Connects similar users across domains via attribute matching and collinearity filtering.
- Attribute-Driven Construction: Ensures semantically meaningful relationships for better knowledge alignment.
Explains the mechanisms for effective knowledge transfer, including Graph Convolutional Networks (GCNs) for intra-domain relationship modeling and an Inter-domain Hierarchical Attention Network (InterHAN) for inter-domain knowledge sharing.
- GCNs for Intra-domain: Captures collaborative signals within each domain.
- InterHAN for Cross-domain: Facilitates multi-aspect message propagation among similar users.
- Shared & Specific Representations: Learns both common and domain-unique user features.
+2.24%
HR@10 Improvement in Low-Overlap Scenarios
SUBCDR significantly boosts recommendation quality in domains with minimal user overlap, outperforming traditional alignment methods by leveraging latent similarities.
Enterprise Process Flow
Generate User Attributes with LLMs
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Construct Heterogeneous Graph
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Aggregate Information (Intra/Inter-domain)
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Learn User Representations
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Generate Recommendations
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| Low-Overlap Scenarios |
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Real-world Impact: Movie & Music Domains
This case study highlights SUBCDR's ability to identify and leverage latent similarities between users across distinct domains (e.g., Movie and Music) without direct overlap. By analyzing user interaction histories with LLMs, SUBCDR generates comprehensive profiles that capture shared, domain-invariant interests (e.g., 'Excitement' and 'Nostalgia'). This enables accurate cross-domain recommendations, such as suggesting the 'Interstellar soundtrack' to a user who enjoys 'Interstellar' and 'Avatar' movies but hasn't explored music in that genre. The framework provides fine-grained recommendations that align with nuanced user preferences, demonstrating superior performance over traditional methods.
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ROI Projection for SUBCDR Implementation
Estimated Annual Savings
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Annual Hours Reclaimed
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Your SUBCDR Implementation Roadmap
A structured approach to integrating SUBCDR into your existing recommendation infrastructure for maximum impact.
Phase 1: Profile Generation & Graph Construction
Utilize Multi-Interests-Aware Prompt Learning with LLMs to generate detailed user profiles. Construct intra-domain bipartite graphs and an inter-domain heterogeneous user similarity graph to bridge similar users.
Phase 2: Knowledge Aggregation & Representation Learning
Apply GCNs for intra-domain relationship modeling and InterHAN for inter-domain knowledge transfer. Learn both shared and specific user representations.
Phase 3: Model Optimization & Deployment
Optimize the SUBCDR model using BCE and regularization losses. Integrate and deploy the enhanced recommendation system into existing platforms, focusing on real-world cold-start scenarios.
Phase 4: Continuous Monitoring & Refinement
Monitor system performance with A/B testing, collect user feedback, and continuously refine LLM prompts and graph structures to adapt to evolving user preferences and data dynamics.
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