This review paper addresses the research question of the significance of explainability in AI
and the role of integrating KG and RL to enhance Explainable Recommender Systems …

Explanations play an essential role in helping users evaluate results from recommender
systems. Various natural language generation methods have been proposed to generate …

Next-point-of-interest (POI) recommendation is a crucial part of location-based social
applications. Existing works have attempted to learn behavior representation through a …

In most recommendation scenarios, user information is difficult to obtain due to user privacy
and data protection issues. Some graph-based methods can learn the user's feature …

A variety of algorithms have been proposed to address the long-standing overestimation
bias problem of Q-learning. Reducing this overestimation bias may lead to an …

Multi-behavior session-based recommendation (MBSBR) is a critical task in e-commerce
and online advertising. By modeling these multiple behaviors, models can better capture the …

Reinforcement learning (RL) has been widely applied in recommendation systems due to its
potential in optimizing the long-term engagement of users. From the perspective of RL …

In sequential recommendation, it is critical to accurately capture the user's intention with
limited session information. Previous work concentrates on modeling a single relationship …