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A Hierarchical Insurance Recommendation Framework Using GraphOLAM Approach

机译：使用GraphOLAM方法的分级保险推荐框架

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摘要

Graph has been widely used for modeling complex relationship datasets in different application fields. Social networks based recommendation system have obtained satisfactory results in Business Intelligence(BI). However, current personalized recommendation methods based on graph structure generally lack interactivity and seldom consider efficient data management. To address these problems, Graph On-Line Analytical Mining (GraphOLAM) is a promising method, which combines OLAP technology with social networks. We first propose an efficient recommendation framework based on GraphOLAM data cube technology for the recommendation in the insurance service. Based on this framework, a new algorithm framework named RU-GOLAM for insurance is proposed, which combines GraphOLAM dimensional aggregation operation and specific recommendation methods. A series of graphs can be generated by GraphOLAM dimensional aggregation operations, which reflect the relationships of nodes under the constraints of different hierarchical dimensions. Node similarities are calculated to generate the Top-N sequential recommendation based on all of these graphs, which can achieve the balance between the topology of the original graph and high-dimensional information of the nodes. Experiments show that our approach outperforms other baseline algorithms on an insurance service dataset.
机译：图表已广泛用于在不同的应用领域建模复杂的关系数据集。基于社交网络的推荐系统在商业智能（BI）中获得了令人满意的结果。但是，基于图形结构的当前个性化推荐方法通常缺乏交互性，很少考虑有效的数据管理。为了解决这些问题，图形在线分析挖掘（Grapholam）是一种有希望的方法，它将OLAP技术与社交网络相结合。我们首先提出了一种基于Grapholam数据立方体技术的高效推荐框架，以便在保险服务中推荐。基于此框架，提出了一种名为Ru-Golam的新算法框架，用于保险，这组合了Grapholam尺寸聚合操作和特定推荐方法。一系列图形可以由Grapholam维聚合操作生成，这反映了在不同层级的约束下的节点的关系。计算节点相似度以基于所有这些图形生成Top-N顺序推荐，这可以实现原始图形与节点的高维信息之间的平衡。实验表明，我们的方法在保险服务数据集上占此了其他基线算法。

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