Reinforcement learning serves as a potent tool for modeling dynamic user interests within
recommender systems, garnering increasing research attention of late. However, a …

Off-policy learning has drawn huge attention in recommender systems (RS), which provides
an opportunity for reinforcement learning to abandon the expensive online training …

Abstract Collaborative Metric Learning (CML) has recently emerged as a popular method in
recommendation systems (RS), closing the gap between metric learning and Collaborative …

Collaborative Metric Learning (CML) has recently emerged as a popular method in
recommendation systems (RS), closing the gap between metric learning and collaborative …

Self-attention architectures, which are rapidly pushing the frontier in natural language
processing, demonstrate a surprising depth-inefficient behavior: Empirical signals indicate …

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 …

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 …

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 …