MMRec: Simplifying Multimodal Recommendation

Abstract

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}.

arXiv commentPublished at MMAsia'23 Workshops

References in corpus (3)

• Learning Transferable Visual Models From Natural Language Supervision
• Bootstrap Latent Representations for Multi-modal Recommendation
• SelfCF: A Simple Framework for Self-supervised Collaborative Filtering

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• Modality Alignment with Multi-scale Bilateral Attention for Multimodal Recommendation
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• RecPFN: Prior-Fitted Networks for In-Context-Based Recommendations