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A Unified Graph Transformer for Overcoming Isolations in Multi-modal Recommendation
However, we argue that existing multi-modal recommender systems typically use isolated processes for both feature extraction and modality modelling.
A Directional Diffusion Graph Transformer for Recommendation
To account for and model possible noise in the users' interactions in graph neural recommenders, we propose a novel Diffusion Graph Transformer (DiffGT) model for top-k recommendation.
Large Multi-modal Encoders for Recommendation
In recent years, the rapid growth of online multimedia services, such as e-commerce platforms, has necessitated the development of personalised recommendation approaches that can encode diverse content about each item.
Contrastive Graph Prompt-tuning for Cross-domain Recommendation
In our research, we introduce the Personalised Graph Prompt-based Recommendation (PGPRec) framework.
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