5 papers
SEP-GCN: Leveraging Similar Edge Pairs with Temporal and Spatial Contexts for Location-Based Recommender Systems
Tan Loc Nguyen, Tin T. Tran
Recommender systems play a crucial role in enabling personalized content delivery amidst the challenges of information overload and human mobility. Although conventional methods of…
Combining social relations and interaction data in Recommender System with Graph Convolution Collaborative Filtering
Tin T. Tran, Vaclav Snasel, Loc Tan Nguyen
A recommender system is an important subject in the field of data mining, where the item rating information from users is exploited and processed to make suitable recommendations w…
CombiGCN: An effective GCN model for Recommender System
Loc Tan Nguyen, Tin T. Tran
Graph Neural Networks (GNNs) have opened up a potential line of research for collaborative filtering (CF). The key power of GNNs is based on injecting collaborative signal into use…
Improvement Graph Convolution Collaborative Filtering with Weighted addition input
Tin T. Tran, V. Snasel
Graph Neural Networks have been extensively applied in the field of machine learning to find features of graphs, and recommendation systems are no exception. The ratings of users o…
BeLightRec: A lightweight recommender system enhanced with BERT
Manh Mai Van, Tin T. Tran
The trend of data mining using deep learning models on graph neural networks has proven effective in identifying object features through signal encoders and decoders, particularly…