mlsanityData to 14 Oct 2026

LLM-Enhanced Multimodal Fusion for Cross-Domain Sequential Recommendation

arXiv 2506.17966 ↗First posted 22 Jun 2025cs.IR cs.CVcited by 11

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Abstract (arXiv)

Cross-Domain Sequential Recommendation (CDSR) predicts user behavior by leveraging historical interactions across multiple domains, focusing on modeling cross-domain preferences and capturing both intra- and inter-sequence item relationships. We propose LLM-Enhanced Multimodal Fusion for Cross-Domain Sequential Recommendation (LLM-EMF), a novel and advanced approach that enhances textual information with Large Language Models (LLM) knowledge and significantly improves recommendation performance through the fusion of visual and textual data. Using the frozen CLIP model, we generate image and text embeddings, thereby enriching item representations with multimodal data. A multiple attention mechanism jointly learns both single-domain and cross-domain preferences, effectively capturing and understanding complex user interests across diverse domains. Evaluations conducted on four e-commerce datasets demonstrate that LLM-EMF consistently outperforms existing methods in modeling cross-domain user preferences, thereby highlighting the effectiveness of multimodal data integration and its advantages in enhancing sequential recommendation systems. Our source code will be released.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 50 of its 82 references that are in the map. First posted on arXiv 22 Jun 2025.

Builds on

50 of 82 references.

References · 50

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38 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 9; 2026: 2.

11 papers.

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  2. 2025DP-FedPGN: Finding Global Flat Minima for Differentially Private Federated Learning via Penalizing Gradient Normcited by 9
  3. 2025SWA-SOP: Spatially-aware Window Attention for Semantic Occupancy Prediction in Autonomous Drivingcited by 0
  4. 2025OC-SOP: Enhancing Vision-Based 3D Semantic Occupancy Prediction by Object-Centric Awarenesscited by 0
  5. 2025Beyond Semantic Understanding: Preserving Collaborative Frequency Components in LLM-based Recommendationcited by 0
  6. 2025A Comprehensive Analysis of Churn Prediction in Telecommunications Using Machine Learningcited by 0
  7. 2025Forestpest-YOLO: A High-Performance Detection Framework for Small Forestry Pestscited by 0
  8. 2025Tag-Enriched Multi-Attention with Large Language Models for Cross-Domain Sequential Recommendationcited by 0
  9. 2025RaX-Crash: A Resource Efficient and Explainable Small Model Pipeline with an Application to City Scale Injury Severity Predictioncited by 0
  10. 2026Generative Data Transformation: From Mixed to Unified Datacited by 0
  11. 2026SQuaD-SQL: Efficient Text-to-SQL with Small Language Models via LLM-Guided Knowledge Distillationcited by 0