Bias CRS下载 - Bias CRS源码下载

Bias CRS

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1.0.0

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通过偏差分析和语言模型增强数据增强改进会话推荐系统

被 EMNLP 2023 接受（调查结果）

准备环境的命令

 apt-get update
apt-get install build-essential -y

Preparing enviroment:
(for torch 1.12.0)

Option 1: 
pip install pyg-lib torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-1.12.0+cu113.html
export LD_LIBRARY_PATH="/opt/conda/lib/:$LD_LIBRARY_PATH"

Option 2:
pip install torch==1.12.0+cu113 torchvision==0.13.0+cu113 torchaudio==0.12.0 --extra-index-url https://download.pytorch.org/whl/cu113 
pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-${TORCH}+${CUDA}.html
pip install torch-sparse -f https://pytorch-geometric.com/whl/torch-${TORCH}+${CUDA}.html
pip install torch-cluster -f https://pytorch-geometric.com/whl/torch-${TORCH}+${CUDA}.html
pip install torch-spline-conv -f https://pytorch-geometric.com/whl/torch-${TORCH}+${CUDA}.html
pip install torch-geometric

pip install -r requirements.txt

For the case of "command 'x86_64-linux-gnu-gcc' failed with exit status 1":
apt-get install python3.x-dev

快速测试

 python run_bias_crs.py --config config/crs/tgredial/tgredial.yaml

实验在 ReDial、KGSF、KBRD 和 TGReDial 模型上进行，并在 ReDIAL 和 TGReDIAL 数据集上进行评估。

数据增强

合成对话的生成和准备首先由 data_aug 文件夹中的 [data_prep_gen_ .ipynb] 和 [gen_convert_ .ipynb] 实现（* 指数据集名称）。

数据增强是在[bias_crs/data/dataloader/base.py]内的base.py中实现的，而通过popNudge对要增强的项目数量的更改可以从这里更改。

每次运行的实验结果都将保存在[data/bias/]目录下，后面是以模型和数据集名称命名的文件夹，标题为[bias_anlytic_data.csv]。

通过跨剧集流行度和面向用户意图的流行度评分对推荐结果进行相应的分析，可以通过[分析]文件夹访问。

计算跨剧集受欢迎程度

 from scipy.stats.stats import pearsonr

def compute_pop_scores(pop_score_dict, items):
    return [pop_score_dict[item] if item in pop_score_dict else 0.0 for item in items]

pop_scores = [compute_pop_scores(pop_score_dict, row['Prediction_items']) for _, row in data.iterrows()]
data['pop_scores'] = pop_scores

new_conv = True
cep_scores = []
for idx, row in data.iterrows():
    # set the default value to the first episode
    if new_conv:
        new_conv = False
        cep_scores.append(0.5)
    else:
        if idx+1 < len(data) and row['conv_id'] != data.at[idx+1, 'conv_id']:
            new_conv=True
        pearsonr_score = np.abs(pearsonr(row['pop_scores'], data.at[idx-1, 'pop_scores'])[0])
        cep_scores.append(pearsonr_score)

data['cep_score'] = cep_scores
data['cep_pop_score'] = data['cep_score'] * data['pop_bias']

计算用户意图导向的流行度

 data['target_pop_score'] = data['target_item_index'].map(pop_score_dict)
data['UIOP'] = np.abs(data['pop_bias'] - data['target_pop_score'])

引文

 @inproceedings{
    title={Improving Conversational Recommendation Systems via Bias Analysis and Language-Model-Enhanced Data Augmentation},
    author={Xi Wang, Hossein A. Rahmani, Jiqun Liu, Emine Yilmaz}
    booktitle={Proceedings of EMNLP 2023 (Findings)}
    year={2023}
}