Skip to content

Latest commit

 

History

History
236 lines (181 loc) · 13.5 KB

README.md

File metadata and controls

236 lines (181 loc) · 13.5 KB

NCR-Paddle

English | 简体中文


require


1 Introduction

This project is an implementation of Neural Collaborative Reasoning based on the PaddlePaddle Framework by Baidu. The NCR project using logical regularization to constrain simple two-layer neural network's behaviors and archives reasoning in user-item uid_embeddings space.

Abstract

Existing Collaborative Filtering (CF) methods are mostly designed based on the idea of matching, i.e., by learning user and item embed- dings from data using shallow or deep models, they try to capture the associative relevance patterns in data, so that a user embedding can be matched with relevant item embeddings using designed or learned similarity functions. However, as a cognition rather than a perception intelligent task, recommendation requires not only the ability of pattern recognition and matching from data, but also the ability of cognitive reasoning in data.

Model architecture

img

Embedding and logical module using two full connect layers

img

Using logical regularization to train OR and NOT module

img

Paper:

  • [1] Chen, H. , Shi, S. , Y Li, & Y Zhang. (2020). Neural collaborative reasoning.

References project:

AIStudio:

2 Metric

NCR model evaluation metric on ML100k dataset

N@5 N@10 HR@5 HR@10
ML100k 0.3794 0.4369 0.5446 0.7208

Pretrained model: Weight

3 Environment

  • hardware:GPU、CPU

  • requirements:

    • PaddlePaddle = 2.1.2
    • numpy==1.18.1
    • pandas==0.24.2
    • scipy==1.3.0
    • tqdm==4.32.1
    • scikit_learn==0.23.1

4 Dataset

Interactions in the datasets are sorted by original timestamp. All the interactions in train comes earlier than those in validation, and then earlier than those in test set. We do positive leave-one-out to keep the last user positive interaction (with rating >3) in the test set. The second to the last positive interaction is in validation set. To guarantee that we do not have cold start problem, we put all the interactions of users who with less than 5 interactions in train set only.

For interactions with label 0 in test sets are not used for evaluation. You can ignore those records since we only want to recommend the items that a user do want to buy. For interactions with label 0 in validation set are also not used for evaluation. However, we need to keep this information in the file so that the negative feedback information can be used as part of the sequence for recommending the item in the test set.

5 Quick start

Step1: clone

# clone this repo
git clone https://github.com/gsq7474741/Paddle-NCR

install requirements

pip install -r requirements.txt

Step2: train

python ./main.py --rank 1 --model_name NCR --optimizer Adam --lr 0.001 --dataset ml100k01-1-5 --metric ndcg@5,ndcg@10,hit@5,hit@10 --max_his 5 --test_neg_n 100 --l2 1e-4 --r_weight 0.1 --random_seed 1 --gpu 1

output:

Test Before Training = 0.0243,0.0432,0.0393,0.0987 ndcg@5,ndcg@10,hit@5,hit@10
Prepare Train Data...
Prepare Validation Data...
Init: 	 train= 0.8473,0.8473 validation= 0.0276,0.0463,0.0393,0.0977 test= 0.0243,0.0432,0.0393,0.0987 [6.9 s] ndcg@5,ndcg@10,hit@5,hit@10
Optimizer: Adam
Epoch     1:   5%|██▌                                              | 22/416 [00:04<01:17,  5.07it/s]

Step3: evaluate & predict

python ./main.py --rank 1 --train 0 --load 1 --model_name NCR --model_path ../model/NCR/0.3653_0.4254_0.5287_0.7144best_test.model --dataset ml100k01-1-5 --metric ndcg@5,ndcg@10,hit@5,hit@10 --max_his 5 --test_neg_n 100 --l2 1e-4 --r_weight 0.1 --random_seed 1 --gpu 0

output:

Test Before Training = 0.0432,0.0612,0.0732,0.1295 ndcg@5,ndcg@10,hit@5,hit@10
Load saved_model from saved_model/NCR/0.3653_0.4254_0.5287_0.7144best_test.model
Test After Training = 0.3794,0.4369,0.5446,0.7208 ndcg@5,ndcg@10,hit@5,hit@10
Save Test Results to result/result.npy

predict result saved in result/result.npy (default dir)

6 Code information

6.1 Code tree

.
├── README.md                                            # readme
├── configs                                              # 配置
│    └── cfg.py                                          # 全局参数
├── data_loaders                                         # dataloaders
│    ├── DataLoader.py                                   #
├── data_processor                                       # 数据预处理
│    ├── DataProcessor.py                                #
│    ├── HisDataProcessor.py                             #
│    ├── ProLogicRecDP.py                                #
├── dataset                                              # 自带数据集
│    ├── 5MoviesTV01-1-5                                 #
│    │   ├── 5MoviesTV01-1-5.test.csv                    #
│    │   ├── 5MoviesTV01-1-5.train.csv                   #
│    │   └── 5MoviesTV01-1-5.validation.csv              #
│    ├── Electronics01-1-5                               #
│    │   ├── Electronics01-1-5.test.csv                  #
│    │   ├── Electronics01-1-5.train.csv                 #
│    │   └── Electronics01-1-5.validation.csv            #
│    ├── README.md                                       #
│    └── ml100k01-1-5                                    #
│        ├── ml100k01-1-5.info.json                      #
│        ├── ml100k01-1-5.test.csv                       #
│        ├── ml100k01-1-5.train.csv                      #
│        ├── ml100k01-1-5.train_group.csv                #
│        ├── ml100k01-1-5.validation.csv                 #
│        └── ml100k01-1-5.vt_group.csv                   #
├── log                                                  # log保存目录
│    └── README.md                                       #
├── main.py                                              # 主程序入口
├── models                                               # 模型代码
│    ├── BaseModel.py                                    # 模型基类
│    ├── CompareModel.py                                 #
│    ├── NCR.py                                          # NCR模型
│    ├── RecModel.py                                     #
├── readme_imgs                                          # readme图片
├── requirements.txt                                     # 依赖包
├── result                                               # 预测结果保存目录
│    ├── README.md                                       #
├── runners                                              # runner代码
│    ├── BaseRunner.py                                   #
│    ├── ProLogicRunner.py                               #
├── saved_model                                          # 模型保存目录
│    ├── NCR                                             #
│    │   ├── 0.3653_0.4254_0.5287_0.7144best_test.model  #
│    └── README.md                                       #
└── utils                                                # 工具
    ├── dataset.py                                       #
    ├── mining.py                                        #
    ├── rank_metrics.py                                  #
    └── utils.py                                         #

6.2 Arguments

args type default help
--load int 0 Whether load saved_model and continue to train
--epoch int 100 Number of epochs.
--check_epoch int 1 Check every epochs.
--early_stop int 1 whether to early-stop.
--lr float 0.01 Learning rate.
--batch_size int 128 Batch size during training.
--eval_batch_size int 128 * 128 Batch size during testing.
--dropout float 0.2 Dropout probability for each deep layer
--l2 float 0.0001 Weight of l2_regularize in loss.
--optimizer str 'GD' optimizer: GD Adam Adagrad
--metric str 'RMSE' metrics: RMSE MAE AUC F1 Accuracy Precision Recall
--skip_eval int 0 number of epochs without evaluation
--gpu str '0' Set CUDA_VISIBLE_DEVICES
--verbose int logging.INFO Logging Level 0 10 ... 50
--log_file str cfg.DEFAULT_LOG Logging file path
--result_file str cfg.DEFAULT_RESULT Result file path
--random_seed int 2022 Random seed of numpy and pytorch
--train int 1 To train the saved_model or not.
--path str 'dataset/' Input data dir.
--dataset str 'ml100k01-1-5' Choose a dataset.
--sep str 't' sep of csv file.
--label str 'label' name of dataset label column.
--model_path str '../saved_model/%s/%s.pdiparams' % (model_name model_name) Model save path.
--u_vector_size int 64 Size of user vectors.
--i_vector_size int 64 Size of item vectors.
--r_weight float 10 Weight of logic regularizer loss
--ppl_weight float 0 Weight of uv interaction prediction loss
--pos_weight float 0 Weight of positive purchase loss
--test_neg_n int 10 Negative sample num for each instance in test/validation set.
--max_his int -1 Max history length.
--sup_his int 0 If sup_his > 0 supplement history list with -1 at the beginning
--sparse_his int 1 Whether use sparse representation of user history.

7 Model information

info
release @gsq7474741
date 2021.09
Framework version Paddle 2.1.2
hardware GPU、CPU
download Weight
online notebook