Sequential Pattern Mining of Large Combinable Items with Values for a Set-of-items Recommendation
Sequential Pattern Mining of Large Combinable Items with Values for a Set-of-items Recommendation
复制标题
具有项目集推荐值的大型可组合项目的顺序模式挖掘
DOI：
10.1109/cbms52027.2021.00017
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Haruo Yokota
中科院分区：
文献类型：
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作者：
Hieu Hanh Le;Yutaka Horino;Tomoyoshi Yamazaki;Kenji Araki;Haruo Yokota
基于序列模式挖掘的下一项推荐方案在实证研究中得到了广泛的应用。然而，目前的解决方案没有考虑涉及具有不同价值的项目的大组合的建议。例如，检查许多样本是了解患者当前健康状况和检查药物处方有效性的关键。通常，一次样本检查可能涉及从一千多个可能的项目中选择数十个检查项目，每个项目与一个测量值相关联。对于不同的项目，值本身会有所不同。基于以往项目值的模式，从大量的候选样本中推荐下一次样本检验，需要对多个检验项目的组合进行有效的处理。本文提出了一种对项目和项目值组合进行矢量化，并识别项目集类型聚类的方法。从最适合目标输入的项目集类型中，推荐使用频率和唯一性的项目集。使用来自一所大学医院的电子病历系统的真实数据，对该方法进行了实验测试。结果表明，该方法能够以最高的准确率和召回率成功地推荐特定的题型。结果的有效性得到了医院医务人员的确认。
Next-item recommendation solutions based on sequential pattern mining have been widely used in empirical studies. However, current solutions do not consider recommendations involving large combinations of items with varied values. For example, inspecting many specimens is key to understanding a patient's current health status and to checking a medical prescription's effectiveness. Typically, a specimen inspection may involve dozens of inspection items selected from more than a thousand possible items, with each item being associated with a measured value. The values themselves will differ for different items. Based on the pattern of previous item values, recommending the next specimen inspection from a huge number of candidate ones, requires that a combination of many inspection items must be processed efficiently. This paper presents a method for vectorizing a combination of items and item values, and identifying clusters of item-set types. From the item-set types that best suit the target input, a set of items is recommended using both frequency and uniqueness. The method was tested experimentally, using real data from a university hospital's electronic medical record system. The results showed that the proposed method can successfully recommend specific item types with the highest precision and recall ratios. The validity of the results was confirmed by the hospital's medical staff.