54 citations · 54 across the 4 of their papers we have counts for
4 papers
Deep Mutual Learning across Task Towers for Effective Multi-Task Recommender Learning
Yi Ren, Ying Du, Bin Wang +1
Recommender systems usually leverage multi-task learning methods to simultaneously optimize several objectives because of the multi-faceted user behavior data. The typical way of c…
Item Cold Start Recommendation via Adversarial Variational Auto-encoder Warm-up
Shenzheng Zhang, Qi Tan, Xinzhi Zheng +2
The gap between the randomly initialized item ID embedding and the well-trained warm item ID embedding makes the cold items hard to suit the recommendation system, which is trained…
Slate-Aware Ranking for Recommendation
Yi Ren, Xiao Han, Xu Zhao +2
We see widespread adoption of slate recommender systems, where an ordered item list is fed to the user based on the user interests and items' content. For each recommendation, the…
Improving Item Cold-start Recommendation via Model-agnostic Conditional Variational Autoencoder
Xu Zhao, Yi Ren, Ying Du +2
Embedding & MLP has become a paradigm for modern large-scale recommendation system. However, this paradigm suffers from the cold-start problem which will seriously compromise the e…