Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 허선 | - |
dc.date.accessioned | 2023-07-12T01:16:22Z | - |
dc.date.available | 2023-07-12T01:16:22Z | - |
dc.date.issued | 2015-02 | - |
dc.identifier.citation | 대한산업공학회지, v. 41, NO. 1, Page. 10-16 | - |
dc.identifier.issn | 1225-0988;2234-6457 | - |
dc.identifier.uri | https://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE06139164 | en_US |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/182893 | - |
dc.description.abstract | In this study, we suggest a method to predict probability distribution of a new customer’s degree of loyalty using C-CRF that reflects the RFM score and similarity to the neighbors of the customer. An RFM score prediction model is introduced to construct the first feature function of C-CRF. Integrating demographical similarity, purchasing characteristic similarity and purchase history similarity, we make a unified similarity variable to configure the second feature function of C-CRF. Then parameters of each feature function are estimated and we train our C-CRF model by training data set and suggest a probabilistic distribution to estimate a new customer’s degree of loyalty. An example is provided to illustrate our model. | - |
dc.language | ko | - |
dc.publisher | 대한산업공학회 | - |
dc.subject | Customer Loyalty | - |
dc.subject | Continuous Conditional Random Field(C-CRF) | - |
dc.subject | RFM Score | - |
dc.subject | Similarity | - |
dc.title | Continuous Conditional Random Field에 의한 인터넷 쇼핑몰 신규 고객등급 예측 | - |
dc.title.alternative | Prediction of New Customer’s Degree of Loyalty of Internet Shopping Mall Using Continuous Conditional Random Field | - |
dc.type | Article | - |
dc.relation.no | 1 | - |
dc.relation.volume | 41 | - |
dc.identifier.doi | 10.7232/JKIIE.2015.41.1.010 | - |
dc.relation.page | 10-16 | - |
dc.relation.journal | 대한산업공학회지 | - |
dc.contributor.googleauthor | 안길승 | - |
dc.contributor.googleauthor | 허선 | - |
dc.sector.campus | E | - |
dc.sector.daehak | 공학대학 | - |
dc.sector.department | 산업경영공학과 | - |
dc.identifier.pid | hursun | - |
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