Papers by Dongyi Zheng

2 papers
FedCSR: A Federated Framework for Multi-Platform Cross-Domain Sequential Recommendation with Dual Contrastive Learning (2025.coling-main)

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Challenge: Existing federated frameworks for cross-domain sequential recommendation rely on user alignment, which increases communication costs and privacy risks.
Approach: They propose a federated cross-domain sequential recommendation framework that eliminates the need for user alignment between platforms.
Outcome: The proposed framework eliminates the need for user alignment between platforms.
Learning Transition Patterns by Large Language Models for Sequential Recommendation (2025.coling-main)

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Challenge: Extensive experiments on six real-world datasets show our approach outperforms the best baselines by 7.33% in NDCG@10, 4.65% in Recall@10 and 8.42% in MRR.
Approach: They propose a framework for mapping sequential item texts to sequential item IDs that incorporates multi-query input and item linear projection to model conditional probability distribution of items.
Outcome: The proposed framework outperforms baseline models on six real-world datasets by 7.33% and 4.65% respectively.

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