most citedOne for All, All for One: Learning and Transferring User Embeddings for Cross-Domain Recommendation

60 citations · 60 across the 2 of their papers we have counts for

collaborators

1 paper

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…