arXiv:2302.03497
공식 코드 · Papers with Code
MMRec: Simplifying Multimodal Recommendation
원문 초록
This paper presents an open-source toolbox, MMRec for multimodal recommendation. MMRec simplifies and canonicalizes the process of implementing and comparing multimodal recommendation models. The objective of MMRec is to provide a unified and configurable arena that can minimize the effort in implementing and testing multimodal recommendation models. It enables multimodal models, ranging from traditional matrix factorization to modern graph-based algorithms, capable of fusing information from multiple modalities simultaneously. Our documentation, examples, and source code are available at \url{https://github.com/enoche/MMRec}.
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Quadratic Interest Network for Multimodal Click-Through Rate Prediction
COHESION: Composite Graph Convolutional Network with Dual-Stage Fusion for Multimodal Recommendation
Collaborative Filtering Meets Spectrum Shift: Connecting User-Item Interaction with Graph-Structured Side Information
Generating with Fairness: A Modality-Diffused Counterfactual Framework for Incomplete Multimodal Recommendations
Dynamic Multimodal Fusion via Meta-Learning Towards Micro-Video Recommendation
Spectrum-based Modality Representation Fusion Graph Convolutional Network for Multimodal Recommendation
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