申論題

二、請具體說明一下,此論文摘的研究主題與內容為何.(40%)
Using information systems and services, many firms accumulate large quantities of dato, which lead to troubles with data procedures, Firms also seek to acquire customer information from large datasets in order to develop accurate market strategies for the adjustment of operational methods. Therefore, in this study, we propose a customer ranking that jeatures a big-data process based on the RFM model (recency, frequency, monetary value). Our aim is to develop a recommendation algorithm that, using an association rule, enhances recommendations and, in turn, enhances the operational effects of firms. We adjust the weight of potential information to perform a customer ranking, which we conduct by using agglomerative hierarchical clustering. Finally, we present recommendations based on the association rule for each customer level. The datasets in this study contain actual sales data. Therefore, the datasets are both authentic and have been proctically applied here. The metrics of evaluation show that this study's recommendation system is highly accurate. This method reduces the operation time that require when processing large quantities of data. The key is the method's use of customer ranking, which can also serve as a decision-maker in firms regarding the implementation of effective marketing strategies. Managers can use the method proposed in this study to calculate customers' values on the basis of potential information and can implement adaptive-recommendation strategies for each type of customer.