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This repository contains the source code for the SIGIR 2023 paper ''Where to Go Next for Recommender Systems? ID- vs. Modality-based Recommender Models Revisited''.
Full version in [Arxiv] or [SIGIR2023].
Invited Talk by Google DeepMind (Slides)
🤗New Resources: 5 Large-scale datasets for evaluating foundation / transferable / multi-modal / LLM recommendaiton models.
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PixelRec(SDM): https://github.com/westlake-repl/PixelRec
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NineRec(TPAMI): https://github.com/westlake-repl/NineRec
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Tenrec(NeurIPS): https://github.com/yuangh-x/2022-NIPS-Tenrec
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AntM2C(SIGIR): https://dl.acm.org/doi/abs/10.1145/3626772.3657865
- torch == 1.7.1+cu110
- torchvision==0.8.2+cu110
- transformers==4.20.1
The complete news recommendation dataset (MIND) is visible under the dataset/MIND
, and the dataset with vision (HM and Bili) requires the following actions:
Download the image file "hm_images.zip" (100,000 images in 3x224x224 size) for HM dataset from this link.
Unzip the downloaded model file hm_images.zip
, then put the unzipped directory hm_images
into dataset/Hm/
for the further processing.
Bili Link: https://drive.google.com/file/d/1k0t3rA4LDVe3RoadoQwU9KPUeG5q8sw4/view?usp=sharing
You need to process the images file of HM dataset to a LMDB database for efficient loading during training.
cd dataset/HM
python run_lmdb_hm.py
We report details of the pre-trained ME we used in Table. Download the pytorch-version of them, and put the checkpoint pytorch_model.bin
into the corresponding path under pretrained_models/
Pre-trained model | #Param. | URL |
---|---|---|
BERTtiny | 4M | https://huggingface.co/prajjwal1/bert-tiny |
BERTsmall | 29M | https://huggingface.co/prajjwal1/bert-small |
BERTbase | 109M | https://huggingface.co/bert-base-uncased |
RoBERTabase | 125M | https://huggingface.co/roberta-base |
OPT125M | 125M | https://huggingface.co/facebook/opt-125M |
ResNet18 | 12M | https://download.pytorch.org/models/resnet18-5c106cde.pth |
ResNet34 | 22M | https://download.pytorch.org/models/resnet34-333f7ec4.pt |
ResNet50 | 26M | https://download.pytorch.org/models/resnet50-19c8e357.pth |
Swin-T | 28M | https://huggingface.co/microsoft/swin-tiny-patch4-window7-224 |
Swin-B | 88M | https://huggingface.co/microsoft/swin-base-patch4-window7-224 |
MAEbase | 86M | https://huggingface.co/facebook/vit-mae-base |
An example: For training text MoRec with SASRec in end2end manner, and using bert-base as the modality encoder:
cd bce_text/main-end2end
python train_bert_base.py
After training, you will get the checkpoint of the MoRec model, then set the parameters in test_bert_base.py
and run it for the test result.
Mentions:
You can change the train_xxx.py
and the test_xxx.py
to set the hyperparameters.
The recommended GPU resource can be found in Table 6 in the paper.
We find that using in-batch debiased cross-entropy loss (Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations (RecSys 2019)) can significantly enhance the performance of IDRec and MoRec compared with the binary cross-entropy loss:
where
Dataset | Metrics | IDRec | BERTsmall | RoBERTabase | Improv. |
---|---|---|---|---|---|
MIND | HR@10 | 22.60 | 22.96 | 23.00 | +1.77% |
MIND | NDCG@10 | 12.57 | 12.82 | 12.82 | +1.99% |
Dataset | Metrics | IDRec | ResNet50 | Swin-B | Improv. |
HM | HR@10 | 11.94 | 11.90 | 12.26 | +2.68% |
HM | NDCG@10 | 7.75 | 7.46 | 7.70 | -0.65% |
Bili | HR@10 | 4.91 | 5.62 | 5.73 | +16.70% |
Bili | NDCG@10 | 2.71 | 3.08 | 3.14 | +15.87% |
It can be seen from the results that both IDRec and MoRec have been greatly improved compared with the binary loss used in our paper. The experiments also showed that the convergence speed was significantly accelerated when using the in-batch debias loss. It is worth noting that under this training setting, the viewpoints in our paper are still confirmed.
We release the code of SASRec with the in-batch debias cross-entropy loss in inbatch_sasrec_e2e_text
and inbatch_sasrec_e2e_vision
, the way of running the codes is the same as described above.
If you use our code or find IDvs.MoRec useful in your work, please cite our paper as:
@inproceedings{yuan2023go,
title = {Where to Go Next for Recommender Systems? ID- vs. Modality-based Recommender Models Revisited},
author = {Yuan, Zheng and Yuan, Fajie and Song, Yu and Li, Youhua and Fu, Junchen and Yang, Fei and Pan, Yunzhu and Ni, Yongxin},
booktitle = {Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {2639–2649},
year = {2023}
}
If you have an innovative idea for building a foundational recommendation model but require a large dataset and computational resources, consider joining our lab as an intern. We can provide access to 100 NVIDIA 80G A100 GPUs and a billion-level dataset of user-video/image/text interactions.
The laboratory is hiring research assistants, interns, doctoral students, and postdoctoral researchers. Please contact the corresponding author for details.
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