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PDRec

The source code is for the paper: Plug-In Diffusion Model for Sequential Recommendation accepted in AAAI 2024 by Haokai Ma, Ruobing Xie, Lei Meng, Xin Chen, Xu Zhang, Leyu Lin and Zhanhui Kang.

Overview

This paper presents a novel Plug-In Diffusion Model for Recommendation (PDRec) framework, which employs the diffusion model as a flexible plugin to jointly take full advantage of the diffusion-generating user preferences on all items. Specifically, PDRec first infers the users' dynamic preferences on all items via a time-interval diffusion model and proposes a Historical Behavior Reweighting (HBR) mechanism to identify the high-quality behaviors and suppress noisy behaviors. In addition to the observed items, PDRec proposes a Diffusion-based Positive Augmentation (DPA) strategy to leverage the top-ranked unobserved items as the potential positive samples, bringing in informative and diverse soft signals to alleviate data sparsity. To alleviate the false negative sampling issue, PDRec employs Noise-free Negative Sampling (NNS) to select stable negative samples for ensuring effective model optimization._

Dependencies

  • Python 3.8.10
  • PyTorch 1.12.0+cu102
  • pytorch-lightning==1.6.5
  • Torchvision==0.8.2
  • Pandas==1.3.5
  • Scipy==1.7.3

Implementation of PDRec

We use the Toy dataset from the Amazon platform and the Book dataset from the Douban platform, you can get this.

Due to the file size limitation, you can download the checkpoints of TI-DiffRec released by us from Google drive and place them in the Checkpoint folder.

PDRec (GRU4Rec) on Toy:

CUDA_VISIBLE_DEVICES=0 python PDRec.py --dataset=amazon_toy --lr 0.005 --temperature 5 --scale_weight 2.0 --scale_max 3.0 --rank_weight 0.1 --candidate_min_percentage_user 50 --top_candidate_coarse_num 50 --top_candidate_fine_num 5 --top_candidate_weight 0.3 --base_model GRU4Rec

PDRec (SASRec) on Toy:

CUDA_VISIBLE_DEVICES=1 python PDRec.py --dataset=amazon_toy --lr 0.005 --temperature 5 --scale_weight 4.0 --scale_max 1.0 --rank_weight 0.1 --candidate_min_percentage_user 90 --top_candidate_coarse_num 50 --top_candidate_fine_num 5 --top_candidate_weight 0.05 --base_model SASRec

PDRec (GRU4Rec) on Book:

CUDA_VISIBLE_DEVICES=2 python PDRec.py --dataset=douban_book --lr 0.01 --temperature 10 --scale_weight 4.0 --scale_max 3.0 --rank_weight 0.3 --candidate_min_percentage_user 80 --top_candidate_coarse_num 100 --top_candidate_fine_num 1 --top_candidate_weight 0.01 --base_model GRU4Rec

PDRec (SASRec) on Book:

CUDA_VISIBLE_DEVICES=3 python PDRec.py --dataset=douban_book --lr 0.001 --temperature 10 --scale_weight 4.0 --scale_max 3.0 --rank_weight 0.5 --candidate_min_percentage_user 80 --top_candidate_coarse_num 100 --top_candidate_fine_num 1 --top_candidate_weight 0.01 --base_model SASRec

TI-DiffRec on Toy

CUDA_VISIBLE_DEVICES=0 python TI_DiffRec.py --lr=5e-5 --dims=[1000] --emb_size=10 --noise_scale=0.01 --noise_min=0.0005 --noise_max=0.005 --reweight=1 --w_min=0.5 --w_max=1.0 --dataset=amazon_toy

TI-DiffRec on Book

CUDA_VISIBLE_DEVICES=0 python TI_DiffRec.py --lr=5e-5 --dims=[256] --emb_size=8 --noise_scale=0.01 --noise_min=0.0005 --noise_max=0.01 --reweight=1 --w_min=0.3 --w_max=1.0 --dataset=douban_book

BibTeX

If you find this work useful for your research, please kindly cite PDRec by:

@inproceedings{PDRec,
  title={Plug-In Diffusion Model for Sequential Recommendation},
  author={Ma, Haokai and Xie, Ruobing and Meng, Lei and Chen, Xin and Zhang, Xu and Lin, Leyu and Kang, Zhanhui},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  year={2024}
}

Acknowledgement

The structure of this code is largely based on DiffRec and SASRec and the dataset is collected by Amazon and RecBole. Thanks for these works.