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Towards a folksonomy graph-based context-aware recommender system of annotated books
Journal of Big Data ( IF 8.6 ) Pub Date : 2021-05-13 , DOI: 10.1186/s40537-021-00457-3
Sara Qassimi , El Hassan Abdelwahed , Meriem Hafidi , Aimad Qazdar

The emergence of collaborative interactions has empowered users by enabling their interactions through tagging practices that create a folksonomy, also called, classification of the shared resources, any identifiable thing or item on the system. In education, tagging is considered a powerful meta-cognitive strategy that successfully engages learners in the learning process. Besides, the collaborative tagging gathers learners’ opinions, thus, provides more comprehensible recommendations. Still, the abundant shared contents are mostly unorganized which makes it hard for users to select and discover the appropriate items of their interests. Thus, the use of recommender systems overcomes the distressing search problem by assisting users in their searching and exploring experience, and suggesting relevant items matching their preferences. In this regard, this article presents a folksonomy graphs based context-aware recommender system (CARS) of annotated books. The generated graphs express the semantic relatedness between these resources, i.e. books, by effectively modeling the folksonomy relationship between user-resource-tag and integrating contextual information within a multi-layer graph referring to a Knowledge Graph (KG). To put our proposal into shape, we model a real-world application of Goodbooks-10k dataset to recommend books. The proposed approach incorporates spectral clustering to deal with the graph partitioning problem. The experimental evaluation shows relevant performance results of graph-based book recommendations.



中文翻译:

迈向带注释的基于民俗解剖图的上下文感知推荐器系统

协作交互的出现使用户能够通过标记实践来实现他们的交互,从而使他们的交互成为可能,这将对共享资源,系统上任何可识别的事物或项目进行民俗分类(也称为分类)。在教育中,标记被认为是一种强大的元认知策略,可以成功地使学习者参与学习过程。此外,协作式标签收集了学习者的意见,从而提供了更易理解的建议。尽管如此,丰富的共享内容大部分还是没有组织的,这使得用户难以选择和发现他们感兴趣的适当项目。因此,推荐系统的使用通过协助用户的搜索和探索经验,并建议与他们的偏好相匹配的相关项目,来克服令人困扰的搜索问题。在这方面,本文介绍了基于民俗解剖图的带注释书籍的情境感知推荐系统(CARS)。生成的图通过有效地建模用户资源标签之间的民俗关系并在引用知识图(KG)的多层图内集成上下文信息,来表达这些资源(即书籍)之间的语义相关性。为了使我们的提案成形,我们对Goodbooks-10k数据集的实际应用程序进行建模以推荐书籍。所提出的方法结合了频谱聚类来处理图划分问题。实验评估显示了基于图形的书本推荐的相关性能结果。生成的图通过有效地建模用户资源标签之间的民俗关系并在引用知识图(KG)的多层图内集成上下文信息,来表达这些资源(即书籍)之间的语义相关性。为了使我们的提案成形,我们对Goodbooks-10k数据集的实际应用程序进行建模以推荐书籍。所提出的方法结合了频谱聚类来处理图划分问题。实验评估显示了基于图形的书本推荐的相关性能结果。生成的图通过有效地建模用户资源标签之间的民俗关系并在引用知识图(KG)的多层图内集成上下文信息,来表达这些资源(即书籍)之间的语义相关性。为了使我们的提案成形,我们对Goodbooks-10k数据集的实际应用程序进行建模以推荐书籍。所提出的方法结合了频谱聚类来处理图划分问题。实验评估显示了基于图形的书本推荐的相关性能结果。我们为Goodbooks-10k数据集的实际应用建模,以推荐书籍。所提出的方法结合了频谱聚类来处理图划分问题。实验评估显示了基于图形的书本推荐的相关性能结果。我们为Goodbooks-10k数据集的实际应用建模,以推荐书籍。所提出的方法结合了频谱聚类来处理图划分问题。实验评估显示了基于图形的书本推荐的相关性能结果。

更新日期:2021-05-13
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