westlake-repl/IDvs.MoRec
End-to-end Training for Multimodal Recommendation Systems
Compares ID-based versus modality-based (text/vision) recommendation architectures through end-to-end training with foundation models like BERT and vision transformers (ResNet, Swin, MAE). Supports sequential recommendation via SASRec integration and includes in-batch debiased cross-entropy loss optimization for improved convergence. Provides reproducible benchmarks across three large-scale datasets (MIND news, H&M fashion, Bilibili video) with downloadable pre-trained encoders from Hugging Face and PyTorch.
166 stars. No commits in the last 6 months.
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166
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22
Language
Python
License
Apache-2.0
Category
Last pushed
Feb 02, 2025
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