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 …

Recommender systems exploit interaction history to estimate user preference, having been
heavily used in a wide range of industry applications. However, static recommendation …

In light of the emergence of deep reinforcement learning (DRL) in recommender systems
research and several fruitful results in recent years, this survey aims to provide a timely and …

While personalization increases the utility of recommender systems, it also brings the issue
of filter bubbles. eg, if the system keeps exposing and recommending the items that the user …

In this study, we propose Shortcut Fine-Tuning (SFT), a new approach for addressing the
challenge of fast sampling of pretrained Denoising Diffusion Probabilistic Models (DDPMs) …

For sequential recommendation, it is essential to capture and predict future or long-term user
preference for generating accurate recommendation over time. To improve the predictive …

Over the years we have seen recommender systems shifting focus from optimizing short-
term engagement toward improving long-term user experience on the platforms. While …

In recent years, Multi-task Learning (MTL) has yielded immense success in Recommender
System (RS) applications [40]. However, current MTL-based recommendation models tend …

Recommender systems have been widely applied in different real-life scenarios to help us
find useful information. In particular, reinforcement learning (RL)-based recommender …

In light of the emergence of deep reinforcement learning (DRL) in recommender systems
research and several fruitful results in recent years, this survey aims to provide a timely and …