17 citations · 28 across the 7 of their papers we have counts for
7 papers
DREAM Technical Report
Bin Zhang, Bowen Zheng, Chao Yi +74
Industrial recommender systems commonly use cascaded retrieval, ranking, and re-ranking pipelines. Although efficient, these pipelines fragment information and objectives across mo…
Uniboost: Global Coordination with Value Alignment for Fair and Efficient Traffic Allocation
Ge Fan, Nan Zhao, Kai Meng +6
With the rapid evolution of internet services, recommendation systems have become indispensable. In particular, the blending (re-ranking) stage plays a pivotal role in allocating t…
MV-HAN: A Hybrid Attentive Networks based Multi-View Learning Model for Large-scale Contents Recommendation
Ge Fan, Chaoyun Zhang, Kai Wang +1
Industrial recommender systems usually employ multi-source data to improve the recommendation quality, while effectively sharing information between different data sources remain a…
QuickSkill: Novice Skill Estimation in Online Multiplayer Games
Chaoyun Zhang, Kai Wang, Hao Chen +4
Matchmaking systems are vital for creating fair matches in online multiplayer games, which directly affects players' satisfactions and game experience. Most of the matchmaking syst…
A collaborative filtering model with heterogeneous neural networks for recommender systems
Ge Fan, Wei Zeng, Shan Sun +3
In recent years, deep neural network is introduced in recommender systems to solve the collaborative filtering problem, which has achieved immense success on computer vision, speec…
Preference Modeling by Exploiting Latent Components of Ratings
Junhua Chen, Wei Zeng, Junming Shao +1
Understanding user preference is essential to the optimization of recommender systems. As a feedback of user's taste, rating scores can directly reflect the preference of a given u…