Recommendations for Explorations based on Graphs

Marialena Kyriakidi, G. Koutrika, Y. Ioannidis
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Abstract

Recommendations are an integral part of data exploration. Existing approaches, however, consider a limited model of recommendations. In this vision paper, we lay the ground for a graph-based approach for recommendations that allows significant flexibility in capturing both data and recommendations and process them efficiently. We determine the requirements of a desired solution and illustrate the overall idea with an example based on the Yelp dataset.
基于图的探索建议
建议是数据探索的一个组成部分。然而,现有的方法考虑的是一个有限的建议模型。在这篇远景论文中,我们为基于图的推荐方法奠定了基础,该方法在捕获数据和建议并有效处理它们方面具有极大的灵活性。我们确定所需解决方案的需求,并使用基于Yelp数据集的示例说明总体思想。
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