Reinforcement learning based recommender systems: A survey
Recommender systems (RSs) have become an inseparable part of our everyday lives. They
help us find our favorite items to purchase, our friends on social networks, and our favorite …
help us find our favorite items to purchase, our friends on social networks, and our favorite …
Sequence-aware recommender systems
Recommender systems are one of the most successful applications of data mining and
machine-learning technology in practice. Academic research in the field is historically often …
machine-learning technology in practice. Academic research in the field is historically often …
Evaluation of session-based recommendation algorithms
Recommender systems help users find relevant items of interest, for example on e-
commerce or media streaming sites. Most academic research is concerned with approaches …
commerce or media streaming sites. Most academic research is concerned with approaches …
Context-aware recommender systems
The importance of contextual information has been recognized by researchers and
practitioners in many disciplines, including e-commerce personalization, information …
practitioners in many disciplines, including e-commerce personalization, information …
Adaptive course recommendation in MOOCs
In the process of course learning, users incline to change their interests with the
improvements of their cognition. Existing course recommendation methods usually assume …
improvements of their cognition. Existing course recommendation methods usually assume …
A survey on reinforcement learning for recommender systems
Recommender systems have been widely applied in different real-life scenarios to help us
find useful information. In particular, reinforcement learning (RL)-based recommender …
find useful information. In particular, reinforcement learning (RL)-based recommender …
Sequential recommendation for optimizing both immediate feedback and long-term retention
In Recommender System (RS) applications, reinforcement learning (RL) has recently
emerged as a powerful tool, primarily due to its proficiency in optimizing long-term rewards …
emerged as a powerful tool, primarily due to its proficiency in optimizing long-term rewards …
Modeling user preferences in recommender systems: A classification framework for explicit and implicit user feedback
Recommender systems are firmly established as a standard technology for assisting users
with their choices; however, little attention has been paid to the application of the user model …
with their choices; however, little attention has been paid to the application of the user model …
Multi-task recommendations with reinforcement learning
In recent years, Multi-task Learning (MTL) has yielded immense success in Recommender
System (RS) applications [40]. However, current MTL-based recommendation models tend …
System (RS) applications [40]. However, current MTL-based recommendation models tend …
Smart E-learning framework for personalized adaptive learning and sequential path recommendations using reinforcement learning
Learning activities are considerably supported and improved by the rapid advancement of e-
learning systems. This gives students a tremendous chance to participate in learning …
learning systems. This gives students a tremendous chance to participate in learning …