Co-occurrence graph neural network for recommender systems

GCRA-Rec: a graph convolutional recurrent attention recommender model for dynamic relevance weighting of historical interactions

Group Semantic Recommendation System using Attention Neural Network

A novel BADLGRS model is evaluated across four datasets and demonstrates superior performance when compared to existing methods, and consistently outperform existing models in group recommendation tasks.

RPGCN: A relation-preserving graph convolutional network for enhanced node feature prediction in recommender systems

Disentangling Multi Behaviors for Recommendation Based on Graph Contrastive Learning

Recommendation systems play a crucial role in efficiently filtering vast amounts of information, and supporting businesses across various domains such as e-commerce, social media, and online entertainment. Traditional recommendation methods, including collaborative filtering, often suffer from data sparsity issues, lim...

Making Collaborative Signals Count: Graph-Aware Large Language Models for Sequential Recommendation

GALLM constructs a collaborative graph over text tokens and item tokens, and models three types of relations that are transformed into lightweight learnable attention biases and incorporated into the LLM attention mechanism, enabling collaborative-aware token interactions without introducing an additional graph encoder...

Graph-based social recommendation with redundant information suppression

A social relationship adjustment loss function, which dynamically adjusts the weights of social connections, and the Hilbert-Schmidt independence criterion loss function, which reduces the dependence between pre- and post-adjusted user/item embeddings, thereby amplifying the effect of adjusted social relationships on t...