ExpLOD: A Framework for Explaining Recommendations based on the Linked Open Data Cloud
ExpLOD: A Framework for Explaining Recommendations based on the Linked Open Data Cloud
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ExpLOD：基于链接开放数据云的解释建议框架
DOI：
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发表时间：
2016
期刊：
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通讯作者：
G. Semeraro
中科院分区：
文献类型：
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作者：
C. Musto;F. Narducci;P. Lops;M. Degemmis;G. Semeraro
在本文中，我们提出了 ExpLOD，这是一个框架，它利用链接开放数据 (LOD) 云中的可用信息来生成推荐算法生成的建议的自然语言解释。该方法基于构建一个图表，其中用户喜欢的项目与通过 LOD 云中可用的属性推荐的项目相关联。接下来，根据这张图，我们实现了一些技术来对这些属性进行排名，并使用最相关的技术来提供用于生成自然语言解释的模块。在实验评估中，我们对 308 名受试者进行了一项用户研究，旨在调查我们的解释框架在多大程度上可以带来更透明、更可信和更有吸引力的推荐。初步结果为我们提供了令人鼓舞的发现，因为我们的算法比非个性化解释基线和基于流行度的算法表现更好。
In this paper we present ExpLOD, a framework which exploits the information available in the Linked Open Data (LOD) cloud to generate a natural language explanation of the suggestions produced by a recommendation algorithm. The methodology is based on building a graph in which the items liked by a user are connected to the items recommended through the properties available in the LOD cloud. Next, given this graph, we implemented some techniques to rank those properties and we used the most relevant ones to feed a module for generating explanations in natural language. In the experimental evaluation we performed a user study with 308 subjects aiming to investigate to what extent our explanation framework can lead to more transparent, trustful and engaging recommendations. The preliminary results provided us with encouraging findings, since our algorithm performed better than both a non-personalized explanation baseline and a popularity-based one.