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MMSSL: Multi-Modal Self-Supervised Learning for Recommendation in WWW' 2023

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MMSSL: Multi-Modal Self-Supervised Learning for Recommendation

PyTorch implementation for WWW 2023 paper Multi-Modal Self-Supervised Learning for Recommendation.

MMSSL

MMSSL is a new multimedia recommender system MMSSL which integrates the generative modality-aware collaborative self-augmentation and the contrastive cross-modality dependency encoding. It achieves better performance than existing SOTA multi-model methods.

Dependencies

Usage

Start training and inference as:

cd MMSSL
python main.py --dataset {DATASET}

Supported datasets: Amazon-Baby, Amazon-Sports, Tiktok, Allrecipes

Datasets

├─ MMSSL/ 
    ├── data/
      ├── tiktok/
      ...
Dataset Amazon Tiktok Allrecipes
Modality V T V T V A T V T
Embed Dim 4096 1048 4096 1048 128 128 768 2048 20
User 35598 19445 9319 19805
Item 18357 7050 6710 10067
Interactions 256308 139110 59541 58922
Sparsity 99.961% 99.899% 99.904% 99.970%
  • We provide processed data at Google Drive. We spend a lot of time collecting datasets, if you want to use our datasets(especially Tiktok), please cite MMSSL in your paper.

Experimental Results

Performance comparison of baselines on different datasets in terms of Recall@20, Precision@20 and NDCG@20:

Baseline Tiktok Amazon-Baby Amazon-Sports Allrecipes
R@20 P@20 N@20 R@20 P@20 N@20 R@20 P@20 N@20 R@20 P@20 N@20
MF-BPR 0.0346 0.0017 0.0130 0.0440 0.0024 0.0200 0.0430 0.0023 0.0202 0.0137 0.0007 0.0053
NGCF 0.0604 0.0030 0.0238 0.0591 0.0032 0.0261 0.0695 0.0037 0.0318 0.0165 0.0008 0.0059
LightGCN 0.0653 0.0033 0.0282 0.0698 0.0037 0.0319 0.0782 0.0042 0.0369 0.0212 0.0010 0.0076
SGL 0.0603 0.0030 0.0238 0.0678 0.0036 0.0296 0.0779 0.0041 0.0361 0.0191 0.0010 0.0069
NCL 0.0658 0.0034 0.0269 0.0703 0.0038 0.0311 0.0765 0.0040 0.0349 0.0224 0.0010 0.0077
HCCF 0.0662 0.0029 0.0267 0.0705 0.0037 0.0308 0.0779 0.0041 0.0361 0.0225 0.0011 0.0082
VBPR 0.0380 0.0018 0.0134 0.0486 0.0026 0.0213 0.0582 0.0031 0.0265 0.0159 0.0008 0.0056
LightGCN-$M$ 0.0679 0.0034 0.0273 0.0726 0.0038 0.0329 0.0705 0.0035 0.0324 0.0235 0.0011 0.0081
MMGCN 0.0730 0.0036 0.0307 0.0640 0.0032 0.0284 0.0638 0.0034 0.0279 0.0261 0.0013 0.0101
GRCN 0.0804 0.0036 0.0350 0.0754 0.0040 0.0336 0.0833 0.0044 0.0377 0.0299 0.0015 0.0110
LATTICE 0.0843 0.0042 0.0367 0.0829 0.0044 0.0368 0.0915 0.0048 0.0424 0.0268 0.0014 0.0103
CLCRec 0.0621 0.0032 0.0264 0.0610 0.0032 0.0284 0.0651 0.0035 0.0301 0.0231 0.0010 0.0093
MMGCL 0.0799 0.0037 0.0326 0.0758 0.0041 0.0331 0.0875 0.0046 0.0409 0.0272 0.0014 0.0102
SLMRec 0.0845 0.0042 0.0353 0.0765 0.0043 0.0325 0.0829 0.0043 0.0376 0.0317 0.0016 0.0118
MMSSL 0.0921 0.0046 0.0392 0.0962 0.0051 0.0422 0.0998 0.0052 0.0470 0.0367 0.0018 0.0135
p-value 1.28e-5 7.12e-6 6.55e-6 2.23e-6 7.69e-6 8.65e-7 7.75e-6 6.48e-6 6.78e-7 3.94e-4 5.06e-6 4.31e-5
Improv. 8.99% 9.52% 6.81% 16.04% 15.91% 14.67% 9.07% 8.33% 10.85% 15.77% 12.50% 14.40%

Ablation study on key components of MMSSL:

Data Amazon-Baby Allrecipes Tiktok
Metrics Recall NDCG Recall NDCG Recall NDCG
w/o-ASL 0.0907 0.0396 0.0326 0.0124 0.0801 0.0358
w/o-CL 0.0924 0.0408 0.0328 0.0130 0.0821 0.0351
w/o-GT 0.0929 0.0405 0.0325 0.0121 0.0815 0.0353
r/p-GAE 0.0931 0.0411 0.0331 0.0126 0.0843 0.0364
MMSSL 0.0962 0.0422 0.0367 0.0135 0.0921 0.0392

Citing

If you find this work is helpful to your research, please consider citing our paper:

@article{wei2023multi,
  title={Multi-Modal Self-Supervised Learning for Recommendation},
  author={Wei, Wei and Huang, Chao and Xia, Lianghao and Zhang, Chuxu},
  journal={arXiv preprint arXiv:2302.10632},
  year={2023}
}

or

@inproceedings{wei2023multi,
  title={Multi-Modal Self-Supervised Learning for Recommendation},
  author={Wei, Wei and Huang, Chao and Xia, Lianghao and Zhang, Chuxu},
  booktitle={Proceedings of the Web Conference (WWW)},
  year={2023}
}

Acknowledgement

The structure of this code is largely based on LATTICE. Thank for their work.

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