The recommender system (RS) has been an integral toolkit of online services. They are
equipped with various deep learning techniques to model user preference based on …

Deep learning has profoundly impacted many areas of machine learning. However, it took a
while for its impact to be felt in the field of recommender systems. In this article, we outline …

In recent years, several music recommendation systems have been developed with the aim
of incorporating valuable information into the user's modeling and recommendation process …

The importance of contextual information has been recognized by researchers and
practitioners in many disciplines, including e-commerce, personalization, information …

Modern recommender systems have evolved rapidly along with deep learning models that
are well-optimized for overall performance, especially those trained under Empirical Risk …

Modeling time-evolving preferences of users with their sequential item interactions, has
attracted increasing attention in many online applications. Hence, sequential recommender …

Reasoning on knowledge graph (KG) has been studied for explainable recommendation
due to its ability of providing explicit explanations. However, current KG-based explainable …

We propose a novel approach for training large language models (LLMs) to adhere to
objectives defined within a latent embedding space. Our method leverages reinforcement …

Nowadays, recommender systems play an increasingly important role in the music scenario.
Generally, music preferences are related to internal and external conditions. For example …

Nowadays, sequential recommendations are becoming more prevalent. A user expects the
system to remember past interactions and not conduct each recommendation round as a …