• Knowledge Management Research
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• v.18 no.4
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• pp.97-118
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• 2017

Thanks to the development of social media, general users become information and knowledge providers. But customers also feel difficulty to decide their purchases due to numerous information. Although recommender systems are trying to solve these information/knowledge overload problem, it may be asked whether they can honestly reflect customers' preferences. Especially, customers in book market consider contents of a book, recency, and price when they make a purchase. Therefore, in this study, we propose a methodology which can reflect these characteristics based on topic modeling and provide proper recommendations to customers in book market. Through experiments, our methodology shows higher performance than traditional collaborative filtering systems. Therefore, we expect that our book recommender system contributes the development of recommender systems studies and positively affect the customer satisfaction and management.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.4
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• pp.462-466
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• 2010

A new buffer cache management scheme that aims at reducing power consumption in mixed HDD and NAND flash memory storage systems is presented. The proposed scheme reduces power consumption by considering different energy-consumption rate of storage devices, I/O operation type (read or write), and reference potential of cached blocks in terms of both recency and frequency. Simulation shows that the proposed scheme reduces power consumption by 18.0% on average and up to 58.9%.

• Proceedings of the Korean Society of Computer Information Conference
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• 2013.07a
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• pp.185-187
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• 2013

유비쿼터스 컴퓨팅이 생활의 일부가 되어가면서 정보의 양도 급속도로 늘어나고 있으며, 이로 인해 많은 데이터 속에서 정보를 찾아내는 기술이 부각되고 있다. 고객 기반의 협력적 필터링을 이용한 고객 선호도 예측 방법에서는 아이템에 대한 사용자의 선호도를 기반으로 이웃 선정 방법을 사용하므로 아이템에 대한 내용을 반영하지 못할 뿐만 아니라 희박성 문제를 해결하지 못하고 있다. 그리고 비슷한 선호도를 가진 일부 아이템의 정보를 바탕으로 하기 때문에 아이템의 속성은 무시하는 경향이 있다. 본 논문에서는 유비쿼터스 상거래에서 RFM(Recency, Frequency, Monetary) 분석 기반의 SOM을 이용한 군집방법을 제안한다. 제안 방법은 고객의 구매 데이터 기반의 유사한 속성의 데이터끼리의 클러스터링을 통해 보다 빠른 시간 내에 고객 성향에 맞는 추천이 가능한 구매 패턴 추출이 가능하다.

• Journal of the Korean Society for Library and Information Science
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• v.30 no.1
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• pp.49-67
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• 1996

The previous study showed a large variation in performance within the queries and suggested that characteristics of queries cotribute to retrieval performance. Three attributes, specificity, complexity and recency were used to analyze the different results within queries. The result showed that subject searching retrieve more relevant documents for a Query with low specificity than a query with high specificity and that queries from the doctoral students' dissertations were specific queries with high specificity.

• Proceedings of the Safety Management and Science Conference
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• 2000.11a
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• pp.287-291
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• 2000

고객의 요구는 더욱더 개성화, 다양화되고 있고, 글로벌 시장은 다양한 가치관, 문화, 전통을 바탕으로 지역적인 개성을 추구한다. 전 세계에 그물처럼 엮어진 인터넷망은 시간과 거리의 제약을 완전히 없앴다. 기업은 국가와 대륙을 초월한 세계 무대에서 치열한 경쟁을 전개하고 있다. 이러한 상황에서 고객의 요구를 파악하여 이에 부합되는 제품의 생산 및 판매가 기업 경영에 있어서 주요 이슈로 등장하고 있으며, 이러한 추세는 제품 개발 기술의 보편화로 고객에 대한 서비스의 차별화로 집중되고 있다. 이런 수요를 충족시키기 해서는 CRM(Customer Relationship Management) 개념이 도입되어야 하고, 제조공정을 개선하며 경쟁력을 강화하기 위해서는 모든 기업이 고객의 가치를 최대화 할 수 있는 동기화 생산이 필수적이라고 하겠다. 본 연구에서는 CRM의 고객정보분류 방법 중 하나인 FART(F;Frequency, R;Recency, A;Amount, T;Type of merchandise/service)를 이용하여 주요고객을 분류하고, 이 정보를 Drum-Buffer-Rope 스케줄링 방법에 반영하여 기업의 생산능력과 우수고객 주문을 고려한 일정계획 방법을 제시한다. 이 결과로 우수고객에 대한 서비스 수준을 향상시키고 궁극적으로는 기업의 이윤을 최대화하는데 기여하고자 한다.

• Journal of the Korea Safety Management & Science
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• v.9 no.6
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• pp.31-38
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• 2007

The construction industry by its nature retains higher level of danger where the smallest of error may lead to a major catastrophe endangering many workers and public safety as well as provoke social criticism. With the aid of quantitative statistical data on safety-related calamity analyzed till this day, this paper handled in depth the analysis of diseases other than the accidents, and selected new variable indexes that influence the indirect sections of calamity that are mostly concealed and incorporated them in a new model. For the factor selection of new calamity evaluation model, FRAT(Frequency, Recency, Amount, Type of merchandise/service)technique of customer-related management in marketing was applied, and as for the significance of each factor in the weight selection of variables. Consequently, considering the graveness of the FRAT itself through safety management experts, the related researches must be expanded.

• 한국디지털정책학회:학술대회논문집
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• 2005.11a
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• pp.45-60
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• 2005

The purpose of this study is to classify customer by e-mailing responsiveness on time-series analysis and testify the effectiveness of grouping by ROI analysis. Response recency, response frequency and Activity(RFA) of e-mailing systems were adapted for Customer segmentations. ROI analysis were consisted of open, click-through, duration time, personalization, conversion rate and email loyalty index of email systems. Major findings are as follows: RFA analysis is used for customer segmentations that is fundamental process of e-CRM applications. Customer segmentations were loyal customer, odds customer, dormant customer, secession customer and observation customer by RFA grouping. Loyal customer group has high point in all ROI index compared to other groups. These results indicated that customer responsiveness of e-mailing systems were appropriate methods to grouping the customer with demographic variables. Therefore, effective e-mailing marketing strategy of e-Biz have suitable active DB and Behavior targeting is best approach to enforcing the target e-mailing marketing.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.803-806
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• 2017

고객의 행동을 분석하기 위한 RFM(Recency, Frequency, Monetary)은 마케팅 분양에서 널리 쓰이고 있는 시작분석기법이다. 최근 축적되는 데이터가 많아지면서 이를 활용하기 위해 기계학습에 대한 관심이 증가하였다. 따라서 RFM 기법과 다양한 알고리즘을 결합하여 데이터를 분석하고자 하는 시도가 이루어지고 있다. 본 논문에서는 RFM 기법과 대표적인 클러스터링 알고리즘인 k-means를 통하여 고객을 등급화 하는 방법에 대해 실험하였다. 기존의 실험에서는 k값을 8 혹은 9로 지정하는 사례가 많았다. 그러나 본 실험에서는 내부평가방법을 통해 데이터 셋에 대한 최적의 k값을 구해보았고, 실험 결과 사용한 4개의 데이터 셋에서 3이라는 동일한 결과가 나왔다.

• Journal of the Korea Society for Simulation
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• v.12 no.2
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• pp.35-44
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• 2003

The effectiveness of buffer cache replacement algorithms is critical to the performance of I/O systems. In this paper, we propose the degree of inter-reference gap (DIG) based block replacement scheme that retains merits of the least recently used (LRU) such as simple implementation and good cache hit ratio (CHR) for general patterns of references, and improves CHR further. In the proposed scheme, cache blocks with low DIGs are distinguished from blocks with high DIGs and the replacement block is selected among high DIGs blocks as done in the low inter-reference recency set (LIRS) scheme. Thus, by having the effect of the partitioning the cache memory dynamically based on DIGs, CHR is improved. Trace-driven simulation is employed to verified the superiority of the DIG based scheme and shows that the performance improves up to about 175% compared to the LRU scheme and 3% compared to the LIRS scheme for the same traces.

• IE interfaces
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• v.25 no.2
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• pp.229-240
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• 2012

Conventional research on customer relationship management(CRM) in general has focused on the effects of individual customer's satisfaction, retention and profit management. However, corporate customers are more profitable than individual customers because of high volume and frequent transactions between companies. In this article, a prototype for a corporate customer relationship management is developed in the postal service. First, the frequency and amount of customers' usage were examined, and thereby the corporate customer rating scheme was established to provide customized service. Second, five different types of usage patterns were determined using clustering analysis. In addition, we presented the rationales behind the five types of patterns. Third, RFM(recency, frequency, monetary) analysis was performed, and then action plans were developed to increase sales. Finally, the prototype software was developed to automatically perform the above analysis using MS Excel program.