Abstract
This study suggests a new product recommendation model to reflect the recent purchasing patterns of customers. There are many methods to measure the similarity between customers or products using one-way collaborative filtering. However, few studies have calculated the similarity of using both customer information and product information. Therefore, in this study, affinity variables that combine customer data with product data are created through a confusion matrix. Various derived variables are also generated to enhance the forecasting performance in enormous analysis data. In this study, various data mining classifiers such as the decision tree, neural network, support vector machine, random forest, and rotation forest are applied, and a sliding-window scheme is considered to construct the recommendation model.
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Kim, H., Yang, G., Jung, H. et al. An Intelligent Product Recommendation Model to Reflect the Recent Purchasing Patterns of Customers. Mobile Netw Appl 24, 163–170 (2019). https://doi.org/10.1007/s11036-017-0986-7
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DOI: https://doi.org/10.1007/s11036-017-0986-7