45 citations · 99 across the 4 of their papers we have counts for
5 papers
Vela: User Behavior as Supervision Sparks LLM Intelligence
Yang Xiao, Yusong Sun, Haoyi Wu +17
Large language models are increasingly deployed in applications that act on users' long-term behavior histories, from personal assistants to recommendation and advertising systems.…
GateDiffInt: Gate-Mediated Controllable Diffusion and Multi-Intent LLM Distillation for User Behavior Modeling
Jialong Duan, Zichen Zhang, Zirui Tu +7
Existing ranking models encode intent only implicitly, making it hard to disentangle structured intents of varying strength and temporal scale. Noise and intent in behavior sequenc…
Personalized Transfer of User Preferences for Cross-domain Recommendation
Yongchun Zhu, Zhenwei Tang, Yudan Liu +5
Cold-start problem is still a very challenging problem in recommender systems. Fortunately, the interactions of the cold-start users in the auxiliary source domain can help cold-st…
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,…
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…