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Generating post hoc review-based natural language justifications for recommender systems
User Modeling and User-Adapted Interaction ( IF 3.6 ) Pub Date : 2020-06-30 , DOI: 10.1007/s11257-020-09270-8
Cataldo Musto , Marco de Gemmis , Pasquale Lops , Giovanni Semeraro

In this article, we present a framework to build post hoc natural language justifications that supports the suggestions generated by a recommendation algorithm. Our methodology is based on the intuition that reviews’ excerpts contain much relevant information that can be used to justify a recommendation; thus, we propose a black-box explanation strategy that takes as input a recommended item and a set of reviews and builds as output a post hoc natural language justification which is completely independent of the underlying recommendation model. To validate our claims, we also introduce three different implementations of our conceptual framework: the first one uses natural language processing and sentiment analysis techniques to identify relevant and distinguishing aspects discussed in the reviews and combines reviews’ excerpts mentioning these aspects in a natural language justification which is presented to the target user. The second implementation extends the first one by introducing automatic aspect extraction and text summarization, which are exploited to generate a unique synthesis presenting the main characteristics of the item that is used as justification. Finally, the third implementation tackles the problem of generating a context-aware justification , that is to say, a justification that differs on varying of the different contextual situations, by automatically learning a lexicon for each contextual setting and by using such a lexicon to diversify the justifications. In the experimental evaluation, we carried out three user studies in different domains, and the results showed that our methodology is able to make the recommendation process more transparent, engaging and trustful for the users, thus confirming the validity of the intuitions behind this work.

中文翻译:

为推荐系统生成基于事后评论的自然语言理由

在本文中,我们提出了一个框架来构建支持推荐算法生成的建议的事后自然语言论证。我们的方法基于这样一种直觉,即评论的摘录包含许多可用于证明推荐合理性的相关信息;因此,我们提出了一种黑盒解释策略,该策略将推荐项目和一组评论作为输入,并构建完全独立于底层推荐模型的事后自然语言证明作为输出。为了验证我们的主张,我们还介绍了我们概念框架的三种不同实现:第一个使用自然语言处理和情感分析技术来识别评论中讨论的相关和有区别的方面,并在呈现给目标用户的自然语言证明中结合评论中提到这些方面的摘录。第二个实现通过引入自动方面提取和文本摘要来扩展第一个实现,利用它们来生成独特的综合,呈现用作理由的项目的主要特征。最后,第三个实现解决了生成上下文感知理由的问题,也就是说,通过自动学习每个上下文设置的词典并使用这样的词典来多样化,在不同的上下文情况下产生不同的理由理由。
更新日期:2020-06-30
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