關鍵字查詢 | 類別:期刊論文 | | 關鍵字:A recommender system to avoid customer churn

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序號 學年期 教師動態
1 97/2 資工系 蔣璿東 教授 期刊論文 發佈 A recommender system to avoid customer churn: A case study , [97-2] :A recommender system to avoid customer churn: A case study期刊論文A recommender system to avoid customer churn: A case studyWang, Yi-Fan; Chiang, Ding-An; Hsu, Mei-Hua; Lin, Cheng-Jung; Lin, I-Long淡江大學資訊工程學系CRM; Data mining; Decision tree; Recommender systemKidlington: PergamonExpert Systems With Applications 36(4), pp.8071-8075A major concern for modern enterprises is to promote customer value, loyalty and contribution through services such as can help establish a long-term, honest relationship with customers. For purposes of better customer relationship management, data mining technology is commonly used to analyze large quantities of data about customer bargains, purchase preferences, customer churn, etc. This paper aims to propose a recommender system for wireless network companies to understand and avoid customer churn. To ensure the accuracy of the analysis, we use the decision tree algorithm to analyze data of over 60,000 transactions and of more than 4000 members, over a period of
2 97/2 資傳系 王亦凡 教授 期刊論文 發佈 A recommender system to avoid customer churn: A case study , [97-2] :A recommender system to avoid customer churn: A case study期刊論文A recommender system to avoid customer churn: A case studyWang, Yi-Fan; Chiang, Ding-An; Hsu, Mei-Hua; Lin, Cheng-Jung; Lin, I-Long淡江大學資訊工程學系CRM; Data mining; Decision tree; Recommender systemKidlington: PergamonExpert Systems With Applications 36(4), pp.8071-8075A major concern for modern enterprises is to promote customer value, loyalty and contribution through services such as can help establish a long-term, honest relationship with customers. For purposes of better customer relationship management, data mining technology is commonly used to analyze large quantities of data about customer bargains, purchase preferences, customer churn, etc. This paper aims to propose a recommender system for wireless network companies to understand and avoid customer churn. To ensure the accuracy of the analysis, we use the decision tree algorithm to analyze data of over 60,000 transactions and of more than 4000 members, over a period of
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