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Building k-partite association graphs for finding recommendation patterns from questionnaire data
Transactions in GIS ( IF 2.568 ) Pub Date : 2021-07-09 , DOI: 10.1111/tgis.12787
Iyke Maduako 1 , Yaqi Gong 2 , Monica Wachowicz 3
Affiliation  

Graph-pattern association rules have been explored for detecting frequent subgraph structures in real-world network data, which can reveal new insights for decision-making, recommender systems, and predictive models. However, questionnaire data have been neglected so far even though they are one of the most affordable ways to gather quantitative data. Questionnaires can cover every aspect of a topic, generating new strategies and trends for many organisations. The challenge is twofold: develop a model for handling nominal/Boolean data and ordinal data simultaneously, as well as multiple values assigned to a single item. In this article, the synergy between the well-known Apriori algorithm and k-partite graph modelling is proposed to discover frequent recommendation patterns from questionnaire data. Using graph centrality and similarity measures, the large number of association rules are further analysed to discover meaningful spatial structures in non-metric spaces. Counting triangles is also used to uncover hidden thematic structures of link recommendations. We demonstrate how our proposed approach can be applied to a tourism questionnaire survey to reveal frequent patterns in k-partite graphs, which can further be used for recommender systems.

中文翻译:

构建 k 部分关联图以从问卷数据中寻找推荐模式

已经探索了图模式关联规则来检测现实世界网络数据中的频繁子图结构,这可以揭示决策、推荐系统和预测模型的新见解。然而,迄今为止,问卷数据一直被忽视,尽管它们是收集定量数据最实惠的方式之一。问卷可以涵盖一个主题的各个方面,为许多组织产生新的战略和趋势。挑战是双重的:开发一个模型来同时处理名义/布尔数据和有序数据,以及分配给单个项目的多个值。在本文中,著名的 Apriori 算法与k之间的协同作用-partite 图建模被提出来从问卷数据中发现频繁的推荐模式。使用图中心性和相似性度量,进一步分析大量关联规则,以发现非度量空间中有意义的空间结构。计数三角形还用于揭示链接推荐的隐藏主题结构。我们展示了我们提出的方法如何应用于旅游问卷调查以揭示k部分图中的频繁模式,这些模式可以进一步用于推荐系统。
更新日期:2021-07-09
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