Multi-Media Recommendation
2 papers with code • 4 benchmarks • 1 datasets
MovieLens
Most implemented papers
MMGCN: Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-video
weiyinwei/mmgcn
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ACM International Conference on Multimedia 2019
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
Liuwq-bit/LightGT
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SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval 2023
Considering its challenges in effectiveness and efficiency, we propose a novel Transformer-based recommendation model, termed as Light Graph Transformer model (LightGT).
