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MMRec: Simplifying Multimodal Recommendation

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arxiv 2302.03497 v2 pith:6EVEXNAY submitted 2023-02-02 cs.IR cs.MM

MMRec: Simplifying Multimodal Recommendation

Xin Zhou This is my paper
classification cs.IR cs.MM
keywords mmrecmultimodalrecommendationmodelsimplementingalgorithmsarenaavailable
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. One Graph, Multiple Gains: Single High-Quality Item-Item Graph for Multimodal Recommendation

    cs.IR 2026-07 conditional novelty 6.0

    A single NCER-refined item-item graph, reused via adaptive gating, UI expansion, and discounted soft-positive BPR, improves multimodal recommendation accuracy and efficiency.

  2. ASPIRE: Make Spectral Graph Collaborative Filtering Great Again via Adaptive Filter Learning

    cs.IR 2026-04 unverdicted novelty 6.0

    ASPIRE learns adaptive graph filters via bi-level optimization to overcome low-frequency explosion bias in spectral collaborative filtering, achieving strong performance and stability.

  3. Well Begun is Half Done: Training-Free and Model-Agnostic Semantically Guaranteed User Representation Initialization for Multimodal Recommendation

    cs.IR 2026-04 unverdicted novelty 5.0

    SG-URInit builds semantically enriched initial user representations for multimodal recommenders by fusing local item modality features with global cluster semantics, closing the gap with item representations without e...