论文标题
基于图的提取器提出建议
Graph-based Extractive Explainer for Recommendations
论文作者
论文摘要
推荐系统中的解释协助用户在一组推荐项目中做出明智的决定。非常重要的研究关注致力于产生自然语言解释，以描述建议的产生方式以及用户为什么要关注它们。但是，由于这些解决方案的不同局限性，例如基于模板或基于世代的限制，因此很难同时使解释易于感知，可靠和个性化。 在这项工作中，我们开发了一个专注的神经网络模型，该模型无缝地集成了用户，项目，属性和句子以进行基于提取的说明。项目的属性被选为中介，以促进通过用户项目特定评估句子相关性的消息传递。为了平衡单个句子相关性，整体属性覆盖范围和内容冗余，我们解决了整数线性编程问题，以最终选择句子。针对两个基准审查数据集的一组最新基线方法的广泛经验评估证明了该解决方案的产生质量。
Explanations in a recommender system assist users in making informed decisions among a set of recommended items. Great research attention has been devoted to generating natural language explanations to depict how the recommendations are generated and why the users should pay attention to them. However, due to different limitations of those solutions, e.g., template-based or generation-based, it is hard to make the explanations easily perceivable, reliable and personalized at the same time. In this work, we develop a graph attentive neural network model that seamlessly integrates user, item, attributes, and sentences for extraction-based explanation. The attributes of items are selected as the intermediary to facilitate message passing for user-item specific evaluation of sentence relevance. And to balance individual sentence relevance, overall attribute coverage, and content redundancy, we solve an integer linear programming problem to make the final selection of sentences. Extensive empirical evaluations against a set of state-of-the-art baseline methods on two benchmark review datasets demonstrated the generation quality of the proposed solution.
