TL;DR
The graph convolutional recurrent attention recommender (GCRA-Rec) model is proposed, which integrates the collaborative filtering strength of GCNs with sequential learning and introduces a novel sequence encoder that dynamically weights historical interactions according to their relevance to the current recommendation target using an attention mechanism.
A novel neural network called the Co-occurrence Graph Neural Network (CoGNN), which utilizes two co-occurrence graphs to establish user and item relationships and outperforms various baseline models in terms of recommendation accuracy and algorithm convergence.
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...
Conversational recommendation aims to predict a user's next potentially interesting item based on a series of historical clicks generated in an anonymous session. In recent years, Graph Convolutional Networks (GCNs) have achieved great success in this field due to their excellent ability to capture complex item transfo...
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.
Graph neural networks (GNNs) have been widely adopted in collaborative filtering to model higher order relationships between users and items. However, existing methods mainly focus on interaction-based structures and neglect the explicit modeling of semantic relations among users and among items. Within these graph str...
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