130 citations · 337 across the 4 of their papers we have counts for
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Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising
Yongchun Zhu, Yudan Liu, Ruobing Xie +6
In recommender systems and advertising platforms, marketers always want to deliver products, contents, or advertisements to potential audiences over media channels such as display,…
Learning to Warm Up Cold Item Embeddings for Cold-start Recommendation with Meta Scaling and Shifting Networks
Yongchun Zhu, Ruobing Xie, Fuzhen Zhuang +5
Recently, embedding techniques have achieved impressive success in recommender systems. However, the embedding techniques are data demanding and suffer from the cold-start problem.…
Transfer-Meta Framework for Cross-domain Recommendation to Cold-Start Users
Yongchun Zhu, Kaikai Ge, Fuzhen Zhuang +5
Cold-start problems are enormous challenges in practical recommender systems. One promising solution for this problem is cross-domain recommendation (CDR) which leverages rich info…
Real-time Attention Based Look-alike Model for Recommender System
Yudan Liu, Kaikai Ge, Xu Zhang +1
Recently, deep learning models play more and more important roles in contents recommender systems. However, although the performance of recommendations is greatly improved, the "Ma…