mlsanityData to 7 Oct 2026

Image Fusion for Cross-Domain Sequential Recommendation

arXiv 2502.15694 ↗First posted 31 Dec 2024cs.IR cs.CV cs.LGcited by 17

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Abstract (arXiv)

Cross-Domain Sequential Recommendation (CDSR) aims to predict future user interactions based on historical interactions across multiple domains. The key challenge in CDSR is effectively capturing cross-domain user preferences by fully leveraging both intra-sequence and inter-sequence item interactions. In this paper, we propose a novel method, Image Fusion for Cross-Domain Sequential Recommendation (IFCDSR), which incorporates item image information to better capture visual preferences. Our approach integrates a frozen CLIP model to generate image embeddings, enriching original item embeddings with visual data from both intra-sequence and inter-sequence interactions. Additionally, we employ a multiple attention layer to capture cross-domain interests, enabling joint learning of single-domain and cross-domain user preferences. To validate the effectiveness of IFCDSR, we re-partitioned four e-commerce datasets and conducted extensive experiments. Results demonstrate that IFCDSR significantly outperforms existing methods.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 32 of its 62 references that are in the map. First posted on arXiv 31 Dec 2024.

Builds on

32 of 62 references.

References · 32

  1. 2017Attention Is All You Needcited by 59,932
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  3. 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,341
  4. 2018Self-Attentive Sequential Recommendationcited by 1,169
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  6. 2017Deep Interest Network for Click-Through Rate Predictioncited by 541
  7. 2021SLIP: Self-supervision meets Language-Image Pre-trainingcited by 434
  8. 2018Session-based Recommendation with Graph Neural Networkscited by 312
  9. 2017Neural Attentive Session-based Recommendationcited by 237
  10. 2022MaskCLIP: Masked Self-Distillation Advances Contrastive Language-Image Pretrainingcited by 187
  11. 2016Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendationcited by 152
  12. 2019Sequential Recommender Systems: Challenges, Progress and Prospectscited by 115
20 more
  1. 2021Sequential Recommendation with Graph Neural Networkscited by 89
  2. 2018CoNet: Collaborative Cross Networks for Cross-Domain Recommendationcited by 81
  3. 2023Leveraging Large Language Models for Sequential Recommendationcited by 78
  4. 2021Contrastive Learning for Cold-Start Recommendationcited by 77
  5. 2019DDTCDR: Deep Dual Transfer Cross Domain Recommendationcited by 71
  6. 2017Translation-based Recommendationcited by 70
  7. 2023Debiased Contrastive Learning for Sequential Recommendationcited by 38
  8. 2021DA-GCN: A Domain-aware Attentive Graph Convolution Network for Shared-account Cross-domain Sequential Recommendationcited by 34
  9. 2023Contrastive Cross-Domain Sequential Recommendationcited by 31
  10. 2020MiNet: Mixed Interest Network for Cross-Domain Click-Through Rate Predictioncited by 30
  11. 2020Mixed Information Flow for Cross-domain Sequential Recommendationscited by 22
  12. 2019Parallel Split-Join Networks for Shared-account Cross-domain Sequential Recommendationscited by 21
  13. 2023Triple Sequence Learning for Cross-domain Recommendationcited by 19
  14. 2024Dual-Hybrid Attention Network for Specular Highlight Removalcited by 18
  15. 2021Dual Attentive Sequential Learning for Cross-Domain Click-Through Rate Predictioncited by 17
  16. 2021RecGURU: Adversarial Learning of Generalized User Representations for Cross-Domain Recommendationcited by 16
  17. 2024Adaptive Patch Contrast for Weakly Supervised Semantic Segmentationcited by 15
  18. 2021Assessing a Single Image in Reference-Guided Image Synthesiscited by 13
  19. 2024High-Fidelity Document Stain Removal via A Large-Scale Real-World Dataset and A Memory-Augmented Transformercited by 12
  20. 2023Dual-interest Factorization-heads Attention for Sequential Recommendationcited by 6

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