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              融合屬性偏好和多階交互信息的可解釋評分預測研究

              鄭建興 李沁文 王素格 李德玉

              鄭建興, 李沁文, 王素格, 李德玉. 融合屬性偏好和多階交互信息的可解釋評分預測研究. 自動化學報, 2021, 48(x): 1?14 doi: 10.16383/j.aas.c210457
              引用本文: 鄭建興, 李沁文, 王素格, 李德玉. 融合屬性偏好和多階交互信息的可解釋評分預測研究. 自動化學報, 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

              融合屬性偏好和多階交互信息的可解釋評分預測研究

              doi: 10.16383/j.aas.c210457
              基金項目: 國家自然科學基金(61632011, 62076158, 62072294, 61603229), 山西省自然科學基金(20210302123468)資助
              詳細信息
                作者簡介:

                鄭建興:山西大學智能信息處理研究所副教授. 主要研究方向為自然語言處理、推薦系統. E-mail: jxzheng@sxu.edu.cn

                李沁文:山西大學計算機與信息技術學院碩士研究生. 主要研究方向為推薦系統. E-mail: 201922404015@email.sxu.edu.cn

                王素格:山西大學智能信息處理研究所教授. 主要研究方向為自然語言處理、情感分析, 本文通訊作者. E-mail: wsg@sxu.edu.cn

                李德玉:山西大學智能信息處理研究所教授. 主要研究方向為數據挖掘. 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: 已有推薦系統主要基于用戶-項目交互矩陣來學習用戶和項目的向量表示, 而當交互矩陣稀疏時, 推薦系統的精度較低, 推薦的結果缺乏可解釋性. 本文考慮了用戶-項目交互行為中的評分標簽信息, 提出了一種融合屬性偏好和多階交互信息的可解釋評分預測方法, 并根據屬性偏好對推薦結果進行了解釋. 首先, 基于注意力機制分析了用戶和項目屬性信息與評分標簽的關系, 建模了節點的屬性偏好特征表示; 然后, 聚合了用戶-項目交互矩陣中節點自身、交互鄰居和評分標簽信息, 通過圖神經網絡學習了節點的多階交互行為特征表示; 最后, 融合了節點的屬性偏好特征和交互行為特征, 在異質類型信息空間下學習了用戶和項目的語義特征表示, 利用多層感知機實現了評分預測, 并在MovieLens和Douban數據集上驗證了方法的有效性. 實驗結果表明, 本文方法在MAE和RMSE指標上有效提高了推薦系統的精度, 緩解了數據稀疏場景下推薦模型性能較低的問題, 提升了推薦結果的可解釋性.
                1)  1 https://grouplens.org/datasets/movielens/2 https://movie.douban.com/
                2)  2 https://movie.douban.com/
              • 圖  1  融合屬性偏好和多階交互信息的評分預測

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

                圖  2  高階交互鄰居的信息傳播

                Fig.  2  Information diffusion of higher-order interaction neighbors

                圖  3  幾種方法在ML-L-S數據集上不同稀疏性的MAE結果

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

                圖  4  幾種方法在ML-L-S數據集上不同稀疏性的RMSE結果

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

                圖  5  幾種方法在ML-1M數據集上不同稀疏性的MAE結果

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

                圖  6  幾種方法在ML-1M數據集上不同稀疏性的RMSE結果

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

                圖  7  幾種方法在Douban數據集上不同稀疏性的MAE結果

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

                圖  8  幾種方法在Douban數據集上不同稀疏性的RMSE結果

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

                圖  9  用戶和電影的評分預測可解釋案例

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

                圖  10  ML-1M數據集上的用戶和電影節點嵌入表示(轉換前)

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

                圖  11  ML-1M數據集上的用戶和電影節點嵌入表示(轉換后)

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

                圖  12  ML-L-S數據集上的用戶和電影節點嵌入表示(轉換前)

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

                圖  13  ML-L-S數據集上的用戶和電影節點嵌入表示(轉換后)

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

                圖  14  Douban數據集上的用戶和電影節點嵌入表示(轉換前)

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

                圖  15  Douban數據集上的用戶和電影節點嵌入表示(轉換后)

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

                表  1  實驗數據集統計信息

                Table  1  Statistical information of experimental datasets

                DatasetsUsersItemsInteractionsRatingSparsity
                ML-L-S61097241008360.5?598.30%
                ML-1M6040388310002091?595.74%
                Douban302269711954931?599.07%
                下載: 導出CSV

                表  2  不同方法在三組數據集上的MAE和RMSE結果

                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
                下載: 導出CSV

                表  3  IncorAttMOIntRec方法在不同嵌入維度下的MAE和RMSE結果

                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
                下載: 導出CSV

                表  4  IncorAttMOIntRec方法在不同注意力維度下的MAE和RMSE結果

                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
                下載: 導出CSV

                表  5  三組數據集上的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
                下載: 導出CSV
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                        • 收稿日期:  2021-05-25
                        • 錄用日期:  2021-08-12
                        • 網絡出版日期:  2022-01-08

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