Multi-Media Recommendation
2 papers with code • 4 benchmarks • 1 datasets
Most implemented papers
MMGCN: Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-video
Existing works on multimedia recommendation largely exploit multi-modal contents to enrich item representations, while less effort is made to leverage information interchange between users and items to enhance user representations and further capture user's fine-grained preferences on different modalities.
LightGT: A Light Graph Transformer for Multimedia Recommendation
Considering its challenges in effectiveness and efficiency, we propose a novel Transformer-based recommendation model, termed as Light Graph Transformer model (LightGT).