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 cstr: 32138.14.j.aas.c210457
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山西大學(xué)智能信息處理研究所 太原 030006
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山西大學(xué)計算機與信息技術(shù)學(xué)院 太原 030006
Research on Explainable Rating Prediction by Fusing Attribute Preference and Multi-order Interaction Information
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Institute of Intelligent Information Processing, Shanxi Un-iversity, Taiyuan 030006
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School of Computer and Information Technology, Shanxi University, Taiyuan 030006
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摘要: 已有推薦系統主要基于用戶(hù)?項目交互矩陣來(lái)學(xué)習用戶(hù)和項目的向量表示, 而當交互矩陣稀疏時(shí), 推薦系統的精度較低, 推薦的結果缺乏可解釋性. 考慮到用戶(hù)?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(jìn)行解釋. 首先, 基于注意力機制分析了用戶(hù)和項目屬性信息與評分標簽的關(guān)系, 建模了節點(diǎn)的屬性偏好特征表示; 然后, 聚合了用戶(hù)?項目交互矩陣中節點(diǎn)自身、交互鄰居和評分標簽信息, 通過(guò)圖神經(jīng)網(wǎng)絡(luò )學(xué)習了節點(diǎn)的多階交互行為特征表示; 最后, 融合了節點(diǎn)的屬性偏好特征和交互行為特征, 在異質(zhì)類(lèi)型信息空間下學(xué)習了用戶(hù)和項目的語(yǔ)義特征表示, 利用多層感知機實(shí)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(shí)驗結果表明, 所提方法在平均絕對誤差(Mean absolute error, MAE)和均方根誤差(Root mean square error, RMSE)指標上有效提高了推薦系統的精度, 緩解了數據稀疏場(chǎng)景下推薦模型性能較低的問(wèn)題, 提升了推薦結果的可解釋性.Abstract: Existing recommender systems mainly learn the vector representation of users and items based on their interaction matrix. However, when the interaction matrix is sparse, the accuracy of recommender systems is low and the recommendation results lack explanation. Considering the rating tag information from user-item interaction behaviors, this paper proposes an explainable rating prediction method by fusing attribute preference and multi-order interaction information, and explains the recommendation results through the attribute preference. First, based on the attention mechanism, we analyze the relationship between attribute information and rating tags for users and items, and model the attribute preference embedding of nodes. Second, by aggregating information about nodes, interactive neighbors and rating tags from user-item interaction matrix, we learn the multi-order interaction behavior embedding of nodes with graph neural networks. Finally, after fusing attribute preference embedding and interaction behavior embedding of nodes, the semantic embeddings of users and items are learned in the heterogeneous type-specific spaces. We make the rating prediction through the multi-layer perceptron and verify the validity of the method on the MovieLens and Douban datasets. The experimental results show that the proposed method can effectively improve the accuracy of recommender systems in mean absolute error (MAE) and root mean square error (RMSE) indexes, which alleviates the problem of poor performance of the model in the scenario of sparse data, and improves the interpretation of recommendation results.1)
1 1https://grouplens.org/datasets/movielens/ 2https://movie.douban.com/ 2)2 2https://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 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 sparsities
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 sparsities
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 sparsities
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 sparsities
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 sparsities
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 sparsities
圖 9 用戶(hù)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(hù)和電影節點(diǎn)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(hù)和電影節點(diǎn)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(hù)和電影節點(diǎn)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(hù)和電影節點(diǎn)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(hù)和電影節點(diǎn)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(hù)和電影節點(diǎn)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(shí)驗數據集統計信息
Table 1 Statistical information of experimental datasets
數據庫 用戶(hù)數 項目數 交互數 評分等級 稀疏度(%) ML-L-S 610 9724 100836 0.5 ~ 5.0 98.30 ML-1M 6040 3883 1000209 1.0 ~ 5.0 95.74 Douban 3022 6971 195493 1.0 ~ 5.0 99.07 
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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
方法 ML-L-S ML-1M Douban MAE RMSE MAE RMSE MAE RMSE UserKNN 0.875 2 1.278 4 0.771 0 0.969 3 0.649 4 0.825 6 ItemKNN 0.680 8 0.886 9 0.739 4 0.925 7 0.697 4 0.872 8 BiasedMF 0.676 9 0.882 4 0.684 5 0.872 4 0.577 5 0.728 4 SVD++ 0.672 4 0.877 0 0.672 9 0.863 3 0.569 0 0.720 0 NCF 0.668 5 0.868 0 0.695 6 0.886 6 0.578 1 0.730 4 AFM 0.665 1 0.867 3 0.688 0 0.873 9 0.564 3 0.713 6 Wide&Deep 0.674 2 0.875 4 0.686 3 0.873 5 0.565 4 0.714 1 ACCM 0.662 8 0.865 7 0.673 4 0.856 6 0.578 9 0.730 1 NGCF 0.664 7 0.866 4 0.682 1 0.869 0 0.576 8 0.727 1 LightGCN 0.662 6 0.861 1 0.675 9 0.857 8 0.570 9 0.721 3 AFN 0.657 9 0.852 5 0.678 0 0.860 4 0.565 5 0.715 2 IncorAtt-
MOIntRec0.645 1* 0.837 2* 0.659 4** 0.843 3** 0.558 3 0.708 0 注: 加粗字體表示各列最優(yōu)結果; 下劃線(xiàn)字體表示各列次優(yōu)結果. “*”表示p值小于0.05; “**”表示p值小于0.01.
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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
嵌入維度 ML-L-S ML-1M Douban MAE RMSE MAE RMSE MAE RMSE 64 0.650 3 0.847 9 0.662 2 0.849 7 0.558 3 0.708 0 128 0.645 1 0.837 2 0.659 5 0.844 6 0.563 7 0.711 7 256 0.648 8 0.844 0 0.659 4 0.843 3 0.568 5 0.717 2 512 0.651 6 0.849 3 0.662 6 0.845 7 0.576 6 0.723 1 注: 加粗字體表示各列最優(yōu)結果.
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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
注意力維度 ML-L-S ML-1M Douban MAE RMSE MAE RMSE MAE RMSE 32 0.653 2 0.848 7 0.666 2 0.847 5 0.566 2 0.714 7 64 0.645 1 0.837 2 0.657 1 0.846 3 0.558 3 0.708 0 128 0.648 6 0.842 4 0.659 4 0.843 3 0.566 9 0.718 6 256 0.650 2 0.846 1 0.659 2 0.845 9 0.573 1 0.722 6 注: 加粗字體表示各列最優(yōu)結果.
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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
變體模型 ML-L-S ML-1M Douban MAE RMSE MAE RMSE MAE RMSE 去掉Rating-tag 0.653 8 0.854 7 0.667 9 0.847 7 0.568 3 0.713 4 去掉Multi-order
interaction0.688 4 0.890 1 0.680 2 0.866 7 0.574 6 0.722 8 去掉Att-preference 0.656 2 0.854 9 0.668 9 0.848 6 0.569 5 0.717 6 去掉Interaction 0.700 7 0.908 7 0.738 1 0.924 5 0.580 3 0.731 9 去掉MLP-
outputlayer0.667 5 0.873 6 0.710 5 0.896 2 0.568 4 0.713 7 IncorAttMOIntRec 0.645 1 0.837 2 0.659 4 0.843 3 0.558 3 0.708 0 注: 加粗字體表示各列最優(yōu)結果.
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