论文标题
邮政处理推荐系统，具有知识图，以获得新兴，受欢迎程度和解释的多样性
Post Processing Recommender Systems with Knowledge Graphs for Recency, Popularity, and Diversity of Explanations
论文作者
论文摘要
现有的可解释的推荐系统主要对推荐产品和已经有经验的产品之间的关系建模，并相应地形状解释类型（例如，由女演员主演的电影“ X”推荐给用户，因为该用户以“ Y”为女演员观看了其他电影）。但是，这些系统都没有研究单个解释的特性（例如，与该女演员的相互作用的重新度）以及有关推荐列表的一组解释（例如，解释类型的多样性）会影响感知到的解释质量。在本文中，我们概念化了三个新颖的特性，这些新型属性对解释的质量进行了建模（链接相互作用的新兴，共享实体流行和解释类型多样性），并提出了能够为这些属性优化的重新排列方法。两个公共数据集的实验表明，我们的方法可以根据拟议的属性（在人群群体之间相当相当）提高解释质量，同时保留建议实用程序。源代码和数据可在https://github.com/giacoballoccu/explanation-quality-recsys上获得。
Existing explainable recommender systems have mainly modeled relationships between recommended and already experienced products, and shaped explanation types accordingly (e.g., movie "x" starred by actress "y" recommended to a user because that user watched other movies with "y" as an actress). However, none of these systems has investigated the extent to which properties of a single explanation (e.g., the recency of interaction with that actress) and of a group of explanations for a recommended list (e.g., the diversity of the explanation types) can influence the perceived explaination quality. In this paper, we conceptualized three novel properties that model the quality of the explanations (linking interaction recency, shared entity popularity, and explanation type diversity) and proposed re-ranking approaches able to optimize for these properties. Experiments on two public data sets showed that our approaches can increase explanation quality according to the proposed properties, fairly across demographic groups, while preserving recommendation utility. The source code and data are available at https://github.com/giacoballoccu/explanation-quality-recsys.
