Explainable Recommender with Geometric Information Bottleneck
Explainable Recommender with Geometric Information Bottleneck
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DOI：
10.1109/tkde.2024.3350447
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发表时间：
2023-05
影响因子：
8.9
通讯作者：
Hanqi Yan;Lin Gui;Menghan Wang;Kun Zhang;Yulan He
中科院分区：
文献类型：
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作者：
Hanqi Yan;Lin Gui;Menghan Wang;Kun Zhang;Yulan He
可解释的推荐系统可以解释他们的推荐决策，增强用户对系统的信任。大多数可解释的推荐系统要么依赖于人类注释的基本原理来训练解释生成的模型，要么利用注意力机制从评论中提取重要的文本跨度作为解释。所提取的基本原理通常局限于单个综述，并且可能无法识别综述文本之外的隐含特征。为了避免昂贵的人类注释过程，并产生超出个人评论的解释，我们建议将几何先验从用户项目交互到一个变分网络推断潜在的因素，从用户项目评论。来自单个用户-项目对的潜在因素可以用于推荐和解释生成，其自然地继承了编码在先验知识中的全局特征。在三个电子商务数据集上的实验结果表明，我们的模型显着提高了使用Wasserstein距离的变分推荐系统的可解释性，同时在推荐行为方面达到了与现有基于内容的推荐系统相当的性能。
Explainable recommender systems can explain their recommendation decisions, enhancing user trust in the systems. Most explainable recommender systems either rely on human-annotated rationales to train models for explanation generation or leverage the attention mechanism to extract important text spans from reviews as explanations. The extracted rationales are often confined to an individual review and may fail to identify the implicit features beyond the review text. To avoid the expensive human annotation process and to generate explanations beyond individual reviews, we propose to incorporate a geometric prior learnt from user-item interactions into a variational network which infers latent factors from user-item reviews. The latent factors from an individual user-item pair can be used for both recommendation and explanation generation, which naturally inherit the global characteristics encoded in the prior knowledge. Experimental results on three e-commerce datasets show that our model significantly improves the interpretability of a variational recommender using the Wasserstein distance while achieving performance comparable to existing content-based recommender systems in terms of recommendation behaviours.