arXiv AI October 27, 2026 By Abdulaziz Samra, Nikita Sukhorukov, Alexey Grishanov, Alexey Vasilev, Evgeny Frolov

Cross-Domain Sequential Recommendation with Representation-Level User Transfer

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arXiv AI

Jun 2

Synthetic Data from Cross-Domain Events for Large-Scale Recommendation Systems

arXiv:2606. 00282v1 Announce Type: cross Abstract: Large-scale recommendation systems operate across diverse domains, yet they face the challenges of data sparsity and noisy implicit feedback.

By Xiangyu Wang, Yawen He, Shivendra Pratap Singh, Han Huang, Mengtong Hu, Sharath Ciddu, Yi-Hsuan Hsieh, Erik Groving, Yi Ding, Jieming Di, Tony Wang, Min Yun, Xiaoyu Chen, Ling Leng, Rob Malkin

llms efficiency

arXiv Machine Learning

Jun 25

TokenMinds: Pretrained User Tokens and Embeddings for User Understanding in Large Recommender Systems

arXiv:2606. 25147v1 Announce Type: cross Abstract: User modeling in industrial recommender systems typically produces dense embeddings, which suffer from representational constraints inherent to fixed-dimensional vectors.

By Qingyun Liu, Bo Yan, Yang Liu, Yuji Roh, Ekansh Sharma, Likang Yin, Emma Olowo, Min-hsuan Tsai, Yuxuan Li, Diego Uribe, Saksham Aggarwal, Siqi Wu, Yuan Hao, Vikas Kedigehalli, Lukasz Heldt, Lichan Hong, Li Wei, Xinyang Yi

llms rag nlp

Hugging Face Trending Papers

Jun 9

Generative Archetype-Grounded Item Representations for Sequential Recommendation

Sequential recommendation aims to predict users' next interaction with items by analyzing their historical behavior. However, the limited quality of item representations remains a critical bottleneck.

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arXiv Machine Learning

Jun 10

Generative Archetype-Grounded Item Representations for Sequential Recommendation

arXiv:2606. 11023v1 Announce Type: cross Abstract: Sequential recommendation aims to predict users' next interaction with items by analyzing their historical behavior.

By Yifan Li, Jiahong Liu, Xinni Zhang, Hao Chen, Yankai Chen, Wenhao Yu, Jianting Chen, Irwin King

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arXiv Machine Learning

Aug 31

Personalized and Multi-View Representation for Federated Cold-Start Recommendation

The paper introduces PMFRec, a federated cold-start recommendation framework that addresses personalization, compositionality, and communication inefficiencies. PMFRec generates user-specific item representations from attribute features, employs a global multi-view encoder with adaptive gating and orthogonality to capture complementary semantics, and fuses collaborative and attribute knowledge into a single exchanged representation. Experiments on real-world datasets demonstrate that PMFRec outperforms strong baselines in cold-item recommendation while improving user-level fairness, warm-scenario adaptability, and robustness under Local Differential Privacy.

By Jaehyung Lim, Wonbin Kweon, Woojoo Kim, Junyoung Kim, Dongha Kim, Hwanjo Yu

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arXiv AI

Jul 28

TextBridgeGNN: Pre-training Graph Neural Network for Cross-Domain Recommendation via Text-Guided Transfer

arXiv:2601. 02366v3 Announce Type: replace-cross Abstract: Graph-based recommendation has achieved great success in recent years.

By Yiwen Chen, Yiqing Wu, Huishi Luo, Fuzhen Zhuang, Deqing Wang, Zhao Zhang

llms rag fine-tuning

One email a morning, machine-written

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Synthetic Data from Cross-Domain Events for Large-Scale Recommendation Systems

TokenMinds: Pretrained User Tokens and Embeddings for User Understanding in Large Recommender Systems

Generative Archetype-Grounded Item Representations for Sequential Recommendation

Generative Archetype-Grounded Item Representations for Sequential Recommendation

Personalized and Multi-View Representation for Federated Cold-Start Recommendation

TextBridgeGNN: Pre-training Graph Neural Network for Cross-Domain Recommendation via Text-Guided Transfer