Sequential Pattern Mining of Large Combinable Items with Values for a Set-of-items Recommendation
Hieu Le, Yutaka Horino, Tomoyoshi Yamazaki, Kenji Araki, Haruo Yokota · 2021
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.