123 citations · 284 across the 9 of their papers we have counts for
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cs.IR2022★ 60 cited
One for All, All for One: Learning and Transferring User Embeddings for Cross-Domain Recommendation
Chenglin Li, Yuanzhen Xie, Chenyun Yu +5
Cross-domain recommendation is an important method to improve recommender system performance, especially when observations in target domains are sparse. However, most existing tech…
cs.IR2021★ 73 cited
RecGURU: Adversarial Learning of Generalized User Representations for Cross-Domain Recommendation
Chenglin Li, Mingjun Zhao, Huanming Zhang +5
Cross-domain recommendation can help alleviate the data sparsity issue in traditional sequential recommender systems. In this paper, we propose the RecGURU algorithm framework to g…