142 citations · 217 across the 12 of their papers we have counts for
7 papers · 1 filter
Temporal aware Multi-Interest Graph Neural Network For Session-based Recommendation
Qi Shen, Shixuan Zhu, Yitong Pang +2
Session-based recommendation (SBR) is a challenging task, which aims at recommending next items based on anonymous interaction sequences. Despite the superior performance of existi…
Automatic Configuration for Optimal Communication Scheduling in DNN Training
Yiqing Ma, Hao Wang, Yiming Zhang +1
ByteScheduler partitions and rearranges tensor transmissions to improve the communication efficiency of distributed Deep Neural Network (DNN) training. The configuration of hyper-p…
Intention Adaptive Graph Neural Network for Category-aware Session-based Recommendation
Chuan Cui, Qi Shen, Shixuan Zhu +4
Session-based recommendation (SBR) is proposed to recommend items within short sessions given that user profiles are invisible in various scenarios nowadays, such as e-commerce and…
Aggressive Q-Learning with Ensembles: Achieving Both High Sample Efficiency and High Asymptotic Performance
Yanqiu Wu, Xinyue Chen, Che Wang +2
Recent advances in model-free deep reinforcement learning (DRL) show that simple model-free methods can be highly effective in challenging high-dimensional continuous control tasks…
Multi-behavior Graph Contextual Aware Network for Session-based Recommendation
Qi Shen, Lingfei Wu, Yitong Pang +4
Predicting the next interaction of a short-term sequence is a challenging task in session-based recommendation (SBR).Multi-behavior session recommendation considers session sequenc…
Heterogeneous Global Graph Neural Networks for Personalized Session-based Recommendation
Yitong Pang, Lingfei Wu, Qi Shen +6
Predicting the next interaction of a short-term interaction session is a challenging task in session-based recommendation. Almost all existing works rely on item transition pattern…
On-Policy Deep Reinforcement Learning for the Average-Reward Criterion
Yiming Zhang, Keith W. Ross
We develop theory and algorithms for average-reward on-policy Reinforcement Learning (RL). We first consider bounding the difference of the long-term average reward for two policie…