X Wang, Q Li, D Yu, Z Wang, H Chen… - Proceedings of the 45th …, 2022 - dl.acm.org
Off-policy learning has drawn huge attention in recommender systems (RS), which provides an opportunity for reinforcement learning to abandon the expensive online training …
S Bao, Q Xu, Z Yang, Y He, X Cao… - Advances in Neural …, 2022 - proceedings.neurips.cc
Abstract Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and Collaborative …
S Bao, Q Xu, Z Yang, Y He, X Cao… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and collaborative …
Y Levine, N Wies, O Sharir, H Bata… - Advances in Neural …, 2020 - proceedings.neurips.cc
Self-attention architectures, which are rapidly pushing the frontier in natural language processing, demonstrate a surprising depth-inefficient behavior: Empirical signals indicate …
C Gao, K Xu, K Zhou, L Li, X Wang, B Yuan… - Proceedings of the 45th …, 2022 - dl.acm.org
Scaling reinforcement learning (RL) to recommender systems (RS) is promising since maximizing the expected cumulative rewards for RL agents meets the objective of RS, ie …
S Bao, Q Xu, Z Yang, X Cao… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
The recently proposed Collaborative Metric Learning (CML) paradigm has aroused wide interest in the area of recommendation systems (RS) owing to its simplicity and …
Deep Q-learning based algorithms have been applied successfully in many decision making problems, while their theoretical foundations are not as well understood. In this paper, we …
X Wang, Q Li, D Yu, G Xu - Proceedings of the ACM Web Conference …, 2022 - dl.acm.org
Reinforcement learning has recently become an active topic in recommender system research, where the logged data that records interactions between items and users …
Most of the existing deep reinforcement learning (RL) approaches for session-based recommendations either rely on costly online interactions with real users, or rely on …