123 citations · 146 across the 5 of their papers we have counts for
10 papers · 1 filter
Sequential Recommendation with User Evolving Preference Decomposition
Weiqi Shao, Xu Chen, Long Xia +2
Modeling user sequential behaviors has recently attracted increasing attention in the recommendation domain. Existing methods mostly assume coherent preference in the same sequence…
User behavior understanding in real world settings
Weiqi Shao, Xu Chen, Jiashu Zhao +2
How to extract meaningful information in user historical behavior plays a crucial role in recommendation. User behavior sequence often contains multiple conceptually distinct items…
Gumble Softmax For User Behavior Modeling
Weiqi Shao, Xu Chen, Jiashu Zhao +2
Recently, sequential recommendation systems are important in solving the information overload in many online services. Current methods in sequential recommendation focus on learnin…
Neural Interactive Collaborative Filtering
Lixin Zou, Long Xia, Yulong Gu +4
In this paper, we study collaborative filtering in an interactive setting, in which the recommender agents iterate between making recommendations and updating the user profile base…
Toward Simulating Environments in Reinforcement Learning Based Recommendations
Xiangyu Zhao, Long Xia, Lixin Zou +2
With the recent advances in Reinforcement Learning (RL), there have been tremendous interests in employing RL for recommender systems. However, directly training and evaluating a n…
Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems
Lixin Zou, Long Xia, Zhuoye Ding +3
Recommender systems play a crucial role in our daily lives. Feed streaming mechanism has been widely used in the recommender system, especially on the mobile Apps. The feed streami…