Faithfully Explainable Recommendation via Neural Logic Reasoning
Faithfully Explainable Recommendation via Neural Logic Reasoning
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DOI：
10.18653/v1/2021.naacl-main.245
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发表时间：
2021-04
期刊：
影响因子：
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通讯作者：
Yaxin Zhu;Yikun Xian;Zuohui Fu;Gerard de Melo;Yongfeng Zhang
中科院分区：
文献类型：
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作者：
Yaxin Zhu;Yikun Xian;Zuohui Fu;Gerard de Melo;Yongfeng Zhang
知识图（KG）在赋予现代推荐系统生成可追溯推理路径来解释推荐过程的能力方面变得越来越重要。然而，先前的研究很少考虑推导解释的可信度来证明决策过程的合理性。据我们所知，这是第一个在KG推理框架下忠实地建模和评估可解释推荐的工作。具体而言，我们提出了可解释推荐（LOGER）的神经逻辑推理，利用可解释逻辑规则来指导解释生成的路径推理过程。我们在电子商务领域的三个大规模数据集上进行了实验，证明了我们的方法在提供高质量推荐以及确定衍生解释的可信度方面的有效性。
Knowledge graphs (KG) have become increasingly important to endow modern recommender systems with the ability to generate traceable reasoning paths to explain the recommendation process. However, prior research rarely considers the faithfulness of the derived explanations to justify the decision-making process. To the best of our knowledge, this is the first work that models and evaluates faithfully explainable recommendation under the framework of KG reasoning. Specifically, we propose neural logic reasoning for explainable recommendation (LOGER) by drawing on interpretable logical rules to guide the path-reasoning process for explanation generation. We experiment on three large-scale datasets in the e-commerce domain, demonstrating the effectiveness of our method in delivering high-quality recommendations as well as ascertaining the faithfulness of the derived explanation.