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融合属性偏好和多阶交互信息的可解释评分预测研究

郑建兴 李沁文 王素格 李德玉

郑建兴, 李沁文, 王素格, 李德玉. 融合属性偏好和多阶交互信息的可解释评分预测研究. 自动化学报, 2021, 48(x): 1−14 doi: 10.16383/j.aas.c210457
引用本文: 郑建兴, 李沁文, 王素格, 李德玉. 融合属性偏好和多阶交互信息的可解释评分预测研究. 自动化学报, 2021, 48(x): 1−14 doi: 10.16383/j.aas.c210457
Zheng Jian-Xing, Li Qin-Wen, Wang Su-Ge, Li De-Yu. Research on explainable rating prediction by fusing attribute preference and multi-order interaction information. Acta Automatica Sinica, 2021, 48(x): 1−14 doi: 10.16383/j.aas.c210457
Citation: Zheng Jian-Xing, Li Qin-Wen, Wang Su-Ge, Li De-Yu. Research on explainable rating prediction by fusing attribute preference and multi-order interaction information. Acta Automatica Sinica, 2021, 48(x): 1−14 doi: 10.16383/j.aas.c210457

融合属性偏好和多阶交互信息的可解释评分预测研究

doi: 10.16383/j.aas.c210457
基金项目: 国家自然科学基金(61632011, 62076158, 62072294, 61603229), 山西省自然科学基金(20210302123468)资助
详细信息
    作者简介:

    郑建兴:山西大学智能信息处理研究所副教授. 主要研究方向为自然语言处理、推荐系统. E-mail: jxzheng@sxu.edu.cn

    李沁文:山西大学计算机与信息技术学院硕士研究生. 主要研究方向为推荐系统. E-mail: 201922404015@email.sxu.edu.cn

    王素格:山西大学智能信息处理研究所教授. 主要研究方向为自然语言处理、情感分析, 本文通讯作者. E-mail: wsg@sxu.edu.cn

    李德玉:山西大学智能信息处理研究所教授. 主要研究方向为数据挖掘. E-mail: lidy@sxu.edu.cn

Research on Explainable Rating Prediction by Fusing Attribute Preference and Multi-order Interaction Information

Funds: Supported by National Natural Science Foundation of P. R. China (61632011, 62076158, 62072294, 61603229), and the Natural Science Foundation of Shanxi Province (20210302123468)
More Information
    Author Bio:

    ZHENG Jian-Xing Associate professor at the Institute of Intelligent Information Processing, Shanxi University. His research interest covers natural language processing and recommender systems

    LI Qin-Wen Master student at the School of Computer and Information Technology, Shanxi University. His research interest covers recommender systems

    WANG Su-Ge Professor at the Institute of Intelligent Information Processing, Shanxi University. Her research interest covers natural language processing and Sentiment Analysis. Corresponding author of this paper

    LI De-Yu Professor at the Institute of Intelligent Information Processing, Shanxi University. His research interest covers data mining

  • 摘要: 已有推荐系统主要基于用户-项目交互矩阵来学习用户和项目的向量表示, 而当交互矩阵稀疏时, 推荐系统的精度较低, 推荐的结果缺乏可解释性. 本文考虑了用户-项目交互行为中的评分标签信息, 提出了一种融合属性偏好和多阶交互信息的可解释评分预测方法, 并根据属性偏好对推荐结果进行了解释. 首先, 基于注意力机制分析了用户和项目属性信息与评分标签的关系, 建模了节点的属性偏好特征表示; 然后, 聚合了用户-项目交互矩阵中节点自身、交互邻居和评分标签信息, 通过图神经网络学习了节点的多阶交互行为特征表示; 最后, 融合了节点的属性偏好特征和交互行为特征, 在异质类型信息空间下学习了用户和项目的语义特征表示, 利用多层感知机实现了评分预测, 并在MovieLens和Douban数据集上验证了方法的有效性. 实验结果表明, 本文方法在MAE和RMSE指标上有效提高了推荐系统的精度, 缓解了数据稀疏场景下推荐模型性能较低的问题, 提升了推荐结果的可解释性.
    1)  1 https://grouplens.org/datasets/movielens/2 https://movie.douban.com/
    2)  2 https://movie.douban.com/
  • 图  1  融合属性偏好和多阶交互信息的评分预测

    Fig.  1  Rating prediction by fusing attribute preference and multi-order interaction information

    图  2  高阶交互邻居的信息传播

    Fig.  2  Information diffusion of higher-order interaction neighbors

    图  3  几种方法在ML-L-S数据集上不同稀疏性的MAE结果

    Fig.  3  MAE results of different methods on ML-L-S dataset with different sparsity

    图  4  几种方法在ML-L-S数据集上不同稀疏性的RMSE结果

    Fig.  4  RMSE results of different methods on ML-L-S dataset with different sparsity

    图  5  几种方法在ML-1M数据集上不同稀疏性的MAE结果

    Fig.  5  MAE results of different methods on ML-1M dataset with different sparsity

    图  6  几种方法在ML-1M数据集上不同稀疏性的RMSE结果

    Fig.  6  RMSE results of different methods on ML-1M dataset with different sparsity

    图  7  几种方法在Douban数据集上不同稀疏性的MAE结果

    Fig.  7  MAE results of different methods on Douban dataset with different sparsity

    图  8  几种方法在Douban数据集上不同稀疏性的RMSE结果

    Fig.  8  RMSE results of different methods on Douban dataset with different sparsity

    图  9  用户和电影的评分预测可解释案例

    Fig.  9  Explainable example of rating prediction for users and movies

    图  10  ML-1M数据集上的用户和电影节点嵌入表示(转换前)

    Fig.  10  The embedding representation of user and movie nodes on ML-1M dataset (before transformation)

    图  11  ML-1M数据集上的用户和电影节点嵌入表示(转换后)

    Fig.  11  The embedding representation of user and movie nodes on ML-1M dataset (after transformation)

    图  12  ML-L-S数据集上的用户和电影节点嵌入表示(转换前)

    Fig.  12  The embedding representation of user and movie nodes on ML-L-S dataset (before transformation)

    图  13  ML-L-S数据集上的用户和电影节点嵌入表示(转换后)

    Fig.  13  The embedding representation of user and movie nodes on ML-L-S dataset (after transformation)

    图  14  Douban数据集上的用户和电影节点嵌入表示(转换前)

    Fig.  14  The embedding representation of user and movie nodes on Douban dataset (before transformation)

    图  15  Douban数据集上的用户和电影节点嵌入表示(转换后)

    Fig.  15  The embedding representation of user and movie nodes on Douban dataset (after transformation)

    表  1  实验数据集统计信息

    Table  1  Statistical information of experimental datasets

    DatasetsUsersItemsInteractionsRatingSparsity
    ML-L-S61097241008360.5−598.30%
    ML-1M6040388310002091−595.74%
    Douban302269711954931−599.07%
    下载: 导出CSV

    表  2  不同方法在三组数据集上的MAE和RMSE结果

    Table  2  MAE and RMSE results of different methods on three datasets.

    MethodML-L-S ML-1M Douban
    MAERMSEMAERMSEMAERMSE
    UserKNN0.87521.2784 0.77100.9693 0.64940.8256
    ItemKNN0.68080.88690.73940.92570.69740.8728
    BiasedMF0.67690.88240.68450.87240.57750.7284
    SVD++0.67240.87700.67290.86330.56900.7200
    NCF0.66850.86800.69560.88660.57810.7304
    AFM0.66510.86730.68800.87390.56430.7136
    Wide&Deep0.67420.87540.68630.87350.56540.7141
    ACCM0.66280.86570.67340.85660.57890.7301
    NGCF0.66470.86640.68210.86900.57680.7271
    LightGCN0.66260.86110.67590.85780.57090.7213
    AFN0.65790.85250.67800.86040.56550.7152
    IncorAttMOIntRec0.6451*0.8372*0.6594**0.8433**0.55830.7080
    *表示p-value p<0.05, **表示p-value p<0.01
    下载: 导出CSV

    表  3  IncorAttMOIntRec方法在不同嵌入维度下的MAE和RMSE结果

    Table  3  MAE and RMSE results for IncorAttMOIntRec method with different embedding dimension sizes

    Embedding sizeML-L-S ML-1M Douban
    MAERMSEMAERMSEMAERMSE
    640.65030.8479 0.66220.8497 0.55830.7080
    1280.64510.83720.65950.84460.56370.7117
    2560.64880.84400.65940.84330.56850.7172
    5120.65160.84930.66260.84570.57660.7231
    下载: 导出CSV

    表  4  IncorAttMOIntRec方法在不同注意力维度下的MAE和RMSE结果

    Table  4  MAE and RMSE results for IncorAttMOIntRec method with different attention dimension sizes

    Attention sizeML-L-S ML-1M Douban
    MAERMSEMAERMSEMAERMSE
    320.65320.8487 0.66620.8475 0.56620.7147
    640.64510.83720.65710.84630.55830.7080
    1280.64860.84240.65940.84330.56690.7186
    2560.65020.84610.65920.84590.57310.7226
    下载: 导出CSV

    表  5  三组数据集上的IncorAttMOIntRec方法消融研究

    Table  5  Ablation study of IncorAttMOIntRec method on three datasets

    MethodML-L-S ML-1MDouban
    MAERMSEMAERMSEMAERMSE
    - Rating-Tag0.65380.85470.66790.84770.56830.7134
    -Multi-Order Interaction0.68840.89010.68020.86670.57460.7228
    -Att-Preference0.65620.85490.66890.84860.56950.7176
    -Interaction0.70070.90870.73810.92450.58030.7319
    -MLP-Outlayer0.66750.87360.71050.89620.56840.7137
    IncorAttMOIntRec0.64510.83720.65940.84330.55830.7080
    下载: 导出CSV
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  • 收稿日期:  2021-05-25
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