In recent years, deep learning (DL) has been the most popular computational approach in
the field of machine learning (ML), achieving exceptional results on a variety of complex …

Deep learning, which is a subfield of machine learning, has opened a new era for the
development of neural networks. The auto-encoder is a key component of deep structure …

In this paper, we present empirical studies on conversational recommendation tasks using
representative large language models in a zero-shot setting with three primary …

Knowledge Graphs (KGs) have been utilized as useful side information to improve
recommendation quality. In those recommender systems, knowledge graph information …

Collaborative Filtering (CF) has emerged as fundamental paradigms for parameterizing
users and items into latent representation space, with their correlative patterns from …

A personalized e-learning system is effective in imparting enhanced learning to its users. As
compared to a conventional e-learning system, which provides similar contents to each …

Recent studies show that graph neural networks (GNNs) are prevalent to model high-order
relationships for collaborative filtering (CF). Towards this research line, graph contrastive …

With the explosive growth of online information, recommender systems play a key role to
alleviate such information overload. Due to the important application value of recommender …

Influenced by the great success of deep learning in computer vision and language
understanding, research in recommendation has shifted to inventing new recommender …

Graph neural networks (GNNs) have recently emerged as an effective collaborative filtering
(CF) approaches for recommender systems. The key idea of GNN-based recommender …