1 citations · 1 across the 3 of their papers we have counts for
5 papers
Semantic-enhanced Co-attention Prompt Learning for Non-overlapping Cross-Domain Recommendation
Lei Guo, Chenlong Song, Feng Guo +3
Non-overlapping Cross-domain Sequential Recommendation (NCSR) is the task that focuses on domain knowledge transfer without overlapping entities. Compared with traditional Cross-do…
Federated Semantic Learning for Privacy-preserving Cross-domain Recommendation
Ziang Lu, Lei Guo, Xu Yu +3
In the evolving landscape of recommender systems, the challenge of effectively conducting privacy-preserving Cross-Domain Recommendation (CDR), especially under strict non-overlapp…
MCRPL: A Pretrain, Prompt & Fine-tune Paradigm for Non-overlapping Many-to-one Cross-domain Recommendation
Hao Liu, Lei Guo, Lei Zhu +3
Cross-domain Recommendation (CR) is the task that tends to improve the recommendations in the sparse target domain by leveraging the information from other rich domains. Existing m…
Prompt-enhanced Federated Content Representation Learning for Cross-domain Recommendation
Lei Guo, Ziang Lu, Junliang Yu +2
Cross-domain Recommendation (CDR) as one of the effective techniques in alleviating the data sparsity issues has been widely studied in recent years. However, previous works may ca…
Motif-Based Prompt Learning for Universal Cross-Domain Recommendation
Bowen Hao, Chaoqun Yang, Lei Guo +2
Cross-Domain Recommendation (CDR) stands as a pivotal technology addressing issues of data sparsity and cold start by transferring general knowledge from the source to the target d…