Research on Explainable Rating Prediction by Fusing Attribute Preference and Multi-order Interaction Information
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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. 考虑到用户−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, 所提方法在平均绝对误差(Mean absolute error, MAE)和均方根误差(Root mean square error, 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.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/ -
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 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
数据库 用户 项目 交互量 分数 稀疏性(%) ML-L-S 610 9724 100836 0.5 ~ 5 98.30 ML-1M 6040 3883 1000209 1 ~ 5 95.74 Douban 3022 6971 195493 1 ~ 5 99.07 
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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
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 0.8866 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: 加粗字体表示各列最优结果; 下划线字体表示各列次优结果. “*”表示p-value小于0.05; “**”表示p-value小于0.01.
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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
嵌入维度 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 0.6488 0.844 0 0.659 4 0.843 3 0.568 5 0.717 2 512 0.6516 0.849 3 0.662 6 0.845 7 0.576 6 0.723 1 注: 加粗字体表示各列最优结果.
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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
注意力维度 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: 加粗字体表示各列最优结果.
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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
方法 ML-L-S ML-1M Douban MAE RMSE MAE RMSE MAE RMSE Rating-tag 0.653 8 0.854 7 0.667 9 0.847 7 0.568 3 0.713 4 Multi-order
interaction0.688 4 0.890 1 0.680 2 0.866 7 0.574 6 0.722 8 Att-preference 0.656 2 0.854 9 0.668 9 0.848 6 0.569 5 0.717 6 Interaction 0.700 7 0.908 7 0.738 1 0.924 5 0.580 3 0.731 9 MLP-outputlayer 0.667 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: 加粗字体表示各列最优结果.
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