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mindspore/model_zoo/official/gnn/bgcf/README.md

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Contents

Bayesian Graph Collaborative Filtering

Bayesian Graph Collaborative Filtering(BGCF) was proposed in 2020 by Sun J, Guo W, Zhang D et al. By naturally incorporating the uncertainty in the user-item interaction graph shows excellent performance on Amazon recommendation dataset.This is an example of training of BGCF with Amazon-Beauty dataset in MindSpore. More importantly, this is the first open source version for BGCF.

Paper: Sun J, Guo W, Zhang D, et al. A Framework for Recommending Accurate and Diverse Items Using Bayesian Graph Convolutional Neural Networks[C]//Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2020: 2030-2039.

Model Architecture

Specially, BGCF contains two main modules. The first is sampling, which produce sample graphs based in node copying. Another module aggregate the neighbors sampling from nodes consisting of mean aggregator and attention aggregator.

Dataset

Note that you can run the scripts based on the dataset mentioned in original paper or widely used in relevant domain/network architecture. In the following sections, we will introduce how to run the scripts using the related dataset below.

  • Dataset size: Statistics of dataset used are summarized as below:

    Amazon-Beauty
    Task Recommendation
    # User 7068 (1 graph)
    # Item 3570
    # Interaction 79506
    # Training Data 60818
    # Test Data 18688
    # Density 0.315%

  • Data Preparation

    • Place the dataset to any path you want, the folder should include files as follows(we use Amazon-Beauty dataset as an example)"
    .
└─data
    ├─ratings_Beauty.csv
    • Generate dataset in mindrecord format for Amazon-Beauty.
    
cd ./scripts
# SRC_PATH is the dataset file path you download.
sh run_process_data_ascend.sh [SRC_PATH]

Features

Mixed Precision

To ultilize the strong computation power of Ascend chip, and accelerate the training process, the mixed training method is used. MindSpore is able to cope with FP32 inputs and FP16 operators. In BGCF example, the model is set to FP16 mode except for the loss calculation part.

Environment Requirements

Quick Start

After installing MindSpore via the official website and Dataset is correctly generated, you can start training and evaluation as follows.

  • running on Ascend

    # run training example with Amazon-Beauty dataset
sh run_train_ascend.sh

# run evaluation example with Amazon-Beauty dataset
sh run_eval_ascend.sh

  • running on GPU

    # run training example with Amazon-Beauty dataset
sh run_train_gpu.sh 0 dataset_path

# run evaluation example with Amazon-Beauty dataset
sh run_eval_gpu.sh 0 dataset_path

Script Description

Script and Sample Code

.
└─bgcf
  ├─README.md
  ├─scripts
  | ├─run_eval_ascend.sh          # Launch evaluation in ascend
  | ├─run_eval_gpu.sh             # Launch evaluation in gpu
  | ├─run_process_data_ascend.sh  # Generate dataset in mindrecord format
  | └─run_train_ascend.sh         # Launch training in ascend
  | └─run_train_gpu.sh            # Launch training in gpu
  |
  ├─src
  | ├─bgcf.py              # BGCF model
  | ├─callback.py          # Callback function
  | ├─config.py            # Training configurations
  | ├─dataset.py           # Data preprocessing
  | ├─metrics.py           # Recommendation metrics
  | └─utils.py             # Utils for training bgcf
  |
  ├─eval.py                # Evaluation net
  └─train.py               # Train net

Script Parameters

Parameters for both training and evaluation can be set in config.py.

  • config for BGCF dataset

    "learning_rate": 0.001,             # Learning rate
"num_epoch": 600,                   # Epoch sizes for training
"num_neg": 10,                      # Negative sampling rate
"raw_neighs": 40,                   # Num of sampling neighbors in raw graph
"gnew_neighs": 20,                  # Num of sampling neighbors in sample graph
"input_dim": 64,                    # User and item embedding dimension
"l2": 0.03                          # l2 coefficient
"neighbor_dropout": [0.0, 0.2, 0.3] # Dropout ratio for different aggregation layer

    config.py for more configuration.

Training Process

Training

  • running on Ascend

    sh run_train_ascend.sh

    Training result will be stored in the scripts path, whose folder name begins with "train". You can find the result like the followings in log.

    Epoch 001 iter 12 loss 34696.242
Epoch 002 iter 12 loss 34275.508
Epoch 003 iter 12 loss 30620.635
Epoch 004 iter 12 loss 21628.908

...
Epoch 597 iter 12 loss 3662.3152
Epoch 598 iter 12 loss 3640.7612
Epoch 599 iter 12 loss 3654.9087
Epoch 600 iter 12 loss 3632.4585
...

  • running on GPU

    sh run_train_gpu.sh 0 dataset_path

    Training result will be stored in the scripts path, whose folder name begins with "train". You can find the result like the followings in log.

    Epoch 001 iter 12 loss 34696.242
Epoch 002 iter 12 loss 34275.508
Epoch 003 iter 12 loss 30620.635
Epoch 004 iter 12 loss 21628.908

Evaluation Process

Evaluation

  • Evaluation on Ascend

    sh run_eval_ascend.sh

    Evaluation result will be stored in the scripts path, whose folder name begins with "eval". You can find the result like the followings in log.

    epoch:020,      recall_@10:0.07345,     recall_@20:0.11193,     ndcg_@10:0.05293,    ndcg_@20:0.06613,
sedp_@10:0.01393,     sedp_@20:0.01126,    nov_@10:6.95106,    nov_@20:7.22280
epoch:040,      recall_@10:0.07410,     recall_@20:0.11537,     ndcg_@10:0.05387,    ndcg_@20:0.06801,
sedp_@10:0.01445,     sedp_@20:0.01168,    nov_@10:7.34799,    nov_@20:7.58883
epoch:060,      recall_@10:0.07654,     recall_@20:0.11987,     ndcg_@10:0.05530,    ndcg_@20:0.07015,
sedp_@10:0.01474,     sedp_@20:0.01206,    nov_@10:7.46553,    nov_@20:7.69436

...
epoch:560,      recall_@10:0.09825,     recall_@20:0.14877,     ndcg_@10:0.07176,    ndcg_@20:0.08883,
sedp_@10:0.01882,     sedp_@20:0.01501,    nov_@10:7.58045,    nov_@20:7.79586
epoch:580,      recall_@10:0.09917,     recall_@20:0.14970,     ndcg_@10:0.07337,    ndcg_@20:0.09037,
sedp_@10:0.01896,     sedp_@20:0.01504,    nov_@10:7.57995,    nov_@20:7.79439
epoch:600,      recall_@10:0.09926,     recall_@20:0.15080,     ndcg_@10:0.07283,    ndcg_@20:0.09016,
sedp_@10:0.01890,     sedp_@20:0.01517,    nov_@10:7.58277,    nov_@20:7.80038
...

  • Evaluation on GPU

    sh run_eval_gpu.sh 0 dataset_path

    Evaluation result will be stored in the scripts path, whose folder name begins with "eval". You can find the result like the followings in log.

    epoch:680,      recall_@10:0.10383,     recall_@20:0.15524,     ndcg_@10:0.07503,    ndcg_@20:0.09249,
sedp_@10:0.01926,     sedp_@20:0.01547,    nov_@10:7.60851,    nov_@20:7.81969

Model Description

Performance

Training Performance

Parameter BGCF Ascend BGCF GPU
Model Version Inception V1 Inception V1
Resource Ascend 910 Tesla V100-PCIE
uploaded Date 09/23/2020(month/day/year) 01/27/2021(month/day/year)
MindSpore Version 1.0.0 1.1.0
Dataset Amazon-Beauty Amazon-Beauty
Training Parameter epoch=600,steps=12,batch_size=5000,lr=0.001 epoch=680,steps=12,batch_size=5000,lr=0.001
Optimizer Adam Adam
Loss Function BPR loss BPR loss
Training Cost 25min 60min
Scripts bgcf script bgcf script

Inference Performance

Parameter BGCF Ascend BGCF GPU
Model Version Inception V1 Inception V1
Resource Ascend 910 Tesla V100-PCIE
uploaded Date 09/23/2020(month/day/year) 01/28/2021(month/day/year)
MindSpore Version 1.0.0 Master(4b3e53b4)
Dataset Amazon-Beauty Amazon-Beauty
Batch_size 5000 5000
Output probability probability
Recall@20 0.1534 0.15524
NDCG@20 0.0912 0.09249

Description of random situation

BGCF model contains lots of dropout operations, if you want to disable dropout, set the neighbor_dropout to [0.0, 0.0, 0.0] in src/config.py.

ModelZoo Homepage

Please check the official homepage.