Abstract:

In order to construct a transparent and trustworthy recommendation mechanism， relevant research works mainly provide reasonable explanations for personalized recommendation through explainable recommendation mechanisms. However， there are three major limitations of the existing explainable recommendation mechanism： 1） using correlations only can provide rational explanations rather than causal explanations， and using paths to provide explanations will bring privacy leakage； 2） the problem of sparse user feedback is ignored， so it is difficult to guarantee the fidelity of explanations； 3） the granularities of explanations are relatively coarse， and users’ personalized preferences are not considered. To solve the above problems， an explainable recommendation mechanism ERCKCI based on Collaborative Knowledge Graph （CKG） and counterfactual inference was proposed. Firstly， based on the user’s own behavior sequence， the counterfactual inference was used to achieve high-sparsity causal decorrelation by using the casual relations， and the counterfactual explanations were derived iteratively. Secondly， in order to improve the fidelity of explanations， not only the CKG and the neighborhood propagation mechanism of the Graph Neural Network （GNN） were used to learn users’ and items’ representations based on single time slice； but also the user long-short term preference were captured to enhance user preference representation through self-attention mechanism on multiple time slices. Finally， via a higher-order connected subgraph of the counterfactual set， the multi-granularity personalized preferences of user was captured to enhance counterfactual explanations. To verify the effectiveness of ERCKCI mechanism， comparison experiments were performed on the public datasets MovieLens（100k）， Book-crossing and MovieLens（1M）. The obtained results show that compared with the Explainable recommendation based on Counterfactual Inference （ECI） algorithm under the Relational Collaborative Filtering （RCF） recommendation model on the first two datasets， the proposed mechanism has the explanation fidelity improved by 4.89 and 3.38 percentage points respectively， the size of CF set reduced by 63.26% and 66.24% respectively， and the sparsity index improved by 1.10 and 1.66 percentage points respectively； so the explainability is improved effectively by the proposed mechanism.

Key words: explainable, counterfactual inference, Collaborative Knowledge Graph (CKG), Graph Neural Network (GNN), recommendation mechanism

摘要：

为构建透明可信的推荐机制，相关研究工作主要通过可解释推荐机制为个性化推荐提供合理解释，然而现有可解释推荐机制存在三大局限：1）利用相关关系只能提供合理化解释而非因果解释，而利用路径提供解释存在隐私泄露问题；2）忽略了用户反馈稀疏的问题，解释的保真度难以保证；3）解释粒度较粗，未考虑用户个性化偏好。为解决上述问题，提出基于协同知识图谱（CKG）与反事实推理的可解释推荐机制（ERCKCI）。首先，基于用户自身的行为序列，采用反事实推理思想利用因果关系实现高稀疏度因果去相关，并迭代推导出反事实解释；其次，为提升解释保真度，不仅在单时间片上利用CKG和图神经网络（GNN）的邻域传播机制学习用户和项目表征，还在多时间片上通过自注意力机制捕获用户长短期偏好以增强用户偏好表征；最后，基于反事实集的高阶连通子图捕获用户的多粒度个性化偏好，从而增强反事实解释。为验证ERCKCI机制的有效性，在公开数据集MovieLens（100k）、Book-crossing和MovieLens（1M）上进行了对比实验。所得结果表明，该机制在前两个数据集上相较于RCF（Relational Collaborative Filtering）推荐模型下的ECI（Explainable recommendation based on Counterfactual Inference），在解释保真度上分别提升了4.89和3.38个百分点，在CF集大小上分别降低了63.26%、66.24%，在稀疏度指标上分别提升了1.10和1.66个百分点，可见该机制能有效提升可解释性。

关键词: 可解释, 反事实推理, 协同知识图谱, 图神经网络, 推荐机制