무료 샘플 판촉의 확장효과 향상을 위한 기계학습 접근법

원하람,안현철 한국경영정보학회 2019 한국경영정보학회 학술대회논문집 Vol.2019 No.11

오늘날 기업은 포화된 시장에서 수익을 창출하기 위하여 다양한 마케팅 전략을 시행하고 있다. 판촉은 마케팅 전략의 하위 전략으로써 구매를 유도하기 위해 고객에게 인센티브를 제공하는 것을 말한다. 본 연구는 판촉방법 중 기업이 가장 쉽게 활용할 수 있는 무료샘플 판촉을 데이터 마이닝의 관점에서 접근하였다. 무료샘플 판촉 주된 목적 중 하나인 확장효과를 보인 고객을 판별하는 다양한 기계학습별 모델을 제안하였으며, 그 결과 XGBoost가 최적의 모델로 선정되었다. 또한 본 연구는 실무적 의의를 더하기 위해 확장효과를 보인 고객의 특성을 파악하였다.

A Recidivism Prediction Model Based on XGBoost Considering Asymmetric Error Costs

원하람,심재승,안현철 한국지능정보시스템학회 2019 지능정보연구 Vol.25 No.1

Recidivism prediction has been a subject of constant research by experts since the early 1970s. But it has become more important as committed crimes by recidivist steadily increase. Especially, in the 1990s, after the US and Canada adopted the 'Recidivism Risk Assessment Report' as a decisive criterion during trial and parole screening, research on recidivism prediction became more active. And in the same period, empirical studies on 'Recidivism Factors' were started even at Korea. Even though most recidivism prediction studies have so far focused on factors of recidivism or the accuracy of recidivism prediction, it is important to minimize the prediction misclassification cost, because recidivism prediction has an asymmetric error cost structure. In general, the cost of misrecognizing people who do not cause recidivism to cause recidivism is lower than the cost of incorrectly classifying people who would cause recidivism. Because the former increases only the additional monitoring costs, while the latter increases the amount of social, and economic costs. Therefore, in this paper, we propose an XGBoost(eXtream Gradient Boosting; XGB) based recidivism prediction model considering asymmetric error cost. In the first step of the model, XGB, being recognized as high performance ensemble method in the field of data mining, was applied. And the results of XGB were compared with various prediction models such as LOGIT(logistic regression analysis), DT(decision trees), ANN(artificial neural networks), and SVM(support vector machines). In the next step, the threshold is optimized to minimize the total misclassification cost, which is the weighted average of FNE(False Negative Error) and FPE(False Positive Error). To verify the usefulness of the model, the model was applied to a real recidivism prediction dataset. As a result, it was confirmed that the XGB model not only showed better prediction accuracy than other prediction models but also reduced the cost of misclassification most effectively.

비대칭 오류 비용을 고려한 XGBoost 기반 재범 예측 모델

원하람,심재승,안현철 한국지능정보시스템학회 2018 한국지능정보시스템학회 학술대회논문집 Vol.2018 No.11

데이터 마이닝을 활용한 재범 가능성 예측 연구

원하람(Ha-Ram Won),심재승(Jae-Seung Shim),안현철(Hyunchul Ahn) 한국IT서비스학회 2018 한국IT서비스학회 학술대회 논문집 Vol.2018 No.-

범죄예측은 더 이상 공상과학 속 이야기가 아니라 정보통신기술과 빅데이터를 통하여 현실화되고 있다. 범죄예측 중에서도 범죄자의 재범 가능성을 예측하는 재범 가능성 예측은 학문적, 실무적 연구가 지속되어왔지만, 여전히 재범률은 높으며 각종 사회적 문제와 비용을 수반하고 있다. 이에 본 연구는 데이터 마이닝 기반의 자동화된 재범 가능성 예측모형을 제안한다. 특히 본 연구 에서는 재범 가능성 예측의 비대칭 오류비용 구조를 고려하여, 적절한 임계값 설정을 통해 재범 가능성 예측의 종합적인 비용을 줄이는 방안에 대해서도 모색한다.

온라인 무료 샘플 판촉의 효과적 활용을 위한 기계학습 기반 고객분류예측 모형

원하람 ( Won Ha-ram ),김무전 ( Kim Moo-jeon ),안현철 ( Ahn Hyunchul ) 한국정보시스템학회 2018 情報시스템硏究 Vol.27 No.3

Purpose The purpose of this study is to build a machine learning-based customer classification model to promote customer expansion effect of the free sample promotion. Specifically, the proposed model classifies potential target customers who are expected to purchase the products included in the free sample promotion after receiving the free samples. Design/methodology/approach This study proposes to build a customer classification model for determining customers suitable for providing free samples by using various machine learning techniques such as logistic regression, multiple discriminant analysis, case-based reasoning, decision tree, artificial neural network, and support vector machine. To validate the usefulness of the proposed model, we apply it to a real-world free sample-based target marketing case of a Korean major cosmetic retail company. Findings Experimental results show that a machine learning-based customer classification model presents satisfactory accuracy ranging from 70% to 75%. In particular, support vector machine is found to be the most effective machine learning technique for free sample-based target marketing model. Our study sheds a light on customer relationship management strategies using free sample promotions.

A Study on the Effect of the Document Summar ization Technique on the Fake News Detection Model

심재승,원하람,안현철 한국지능정보시스템학회 2019 지능정보연구 Vol.25 No.3

Fake news has emerged as a significant issue over the last few years, igniting discussions and research on how to solve this problem. In particular, studies on automated fact-checking and fake news detection using artificial intelligence and text analysis techniques have drawn attention. Fake news detection research entails a form of document classification; thus, document classification techniques have been widely used in this type of research. However, document summarization techniques have been inconspicuous in this field. At the same time, automatic news summarization services have become popular, and a recent study found that the use of news summarized through abstractive summarization has strengthened the predictive performance of fake news detection models. Therefore, the need to study the integration of document summarization technology in the domestic news data environment has become evident. In order to examine the effect of extractive summarization on the fake news detection model, we first summarized news articles through extractive summarization. Second, we created a summarized news-based detection model. Finally, we compared our model with the full-text-based detection model. The study found that BPN(Back Propagation Neural Network) and SVM(Support Vector Machine) did not exhibit a large difference in performance; however, for DT(Decision Tree), the full-text-based model demonstrated a somewhat better performance. In the case of LR(Logistic Regression), our model exhibited the superior performance. Nonetheless, the results did not show a statistically significant difference between our model and the full-text-based model. Therefore, when the summary is applied, at least the core information of the fake news is preserved, and the LR-based model can confirm the possibility of performance improvement. This study features an experimental application of extractive summarization in fake news detection research by employing various machine-learning algorithms. The study’s limitations are, essentially, the relatively small amount of data and the lack of comparison between various summarization technologies. Therefore, an in-depth analysis that applies various analytical techniques to a larger data volume would be helpful in the future.

국내 뉴스 기사를 활용한 Doc2vec기반 지능형 가짜뉴스 분류모델

심재승,원하람,안현철 한국지능정보시스템학회 2019 한국지능정보시스템학회 학술대회논문집 Vol.2019 No.6

사용자의 정량 데이터와 정성 데이터를 동시에 고려하는 협업 필터링 기반 추천 시스템

심재승,원하람,이윤주,안현철 한국경영정보학회 2019 한국경영정보학회 학술대회논문집 Vol.2019 No.5

고객 키워드 검색 데이터를 활용한 Word2Vec 기반 협업필터링

이윤주,원하람,김지수,이영은,남재건,안현철 한국지능정보시스템학회 2019 한국지능정보시스템학회 학술대회논문집 Vol.2019 No.6

A Hybrid Collaborative Filtering-based Product Recommender System using Search Keywords

Yunju Lee(이윤주),Haram Won(원하람),Jaeseung Shim(심재승),Hyunchul Ahn(안현철) 한국지능정보시스템학회 2020 지능정보연구 Vol.26 No.1

추천시스템(recommender system)은 고객의 선호도를 예측하여 상품과 서비스를 제공하는 기법으로, 현재 다양한 온라인 서비스에 활용되고 있다. 이와 관련된 많은 선행 연구들은 협업필터링(collaborative filtering)에 기반한 추천시스템을 제안하였는데, 대부분의 경우 고객의 구매 내역 또는 평점 데이터만 사용하여 진행되었다. 오늘날 소비자들은 제품을 구매하는 과정에서 온라인 검색 행동을 하여 관심있는 제품을 찾는다. 그렇기 때문에 검색 키워드 데이터는 고객의 선호도를 파악하는데 매우 유용한 정보일 수 있다. 그러나 지금까지 추천시스템 연구에서 사용되는 경우는 거의 없었다. 이에 본 연구는 고객의 검색 행동에 주목하여 온라인 쇼핑몰 고객의 검색 키워드 데이터와 구매 데이터를 고려한 하이브리드 협업 필터링을 제안하였다. 본 연구는 제안된 모델의 적용 가능성을 검증하기 위해 실제 온라인 쇼핑몰 데이터를 사용하여 성능을 검증하였다. 연구 결과, 추천 상품의 개수가 많아질수록 고객의 검색 키워드를 기반으로 구축된 협업필터링의 추천 성능이 향상되는 반면 일반적인 협업필터링의 성능은 추천된 상품의 개수가 많아질수록 점차 감소함을 발견하였다. 따라서 본 연구는 검색 키워드 데이터를 활용한 하이브리드 협업필터링이 고객의 선호도를 반영한 추천할 수 있으며, 구매이력 데이터의 정보부족을 해결할 수 있음을 확인하였다. 이는 기존의 정량 데이터만을 활용한 추천 시스템이 아닌, 비정형 데이터인 텍스트를 사용함으로써 새로운 하이브리드 협업필터링 구축 방법을 제안했다는 점에서 의의가 있다. A recommender system is a system that recommends products or services that best meet the preferences of each customer using statistical or machine learning techniques. Collaborative filtering (CF) is the most commonly used algorithm for implementing recommender systems. However, in most cases, it only uses purchase history or customer ratings, even though customers provide numerous other data that are available. E-commerce customers frequently use a search function to find the products in which they are interested among the vast array of products offered. Such search keyword data may be a very useful information source for modeling customer preferences. However, it is rarely used as a source of information for recommendation systems. In this paper, we propose a novel hybrid CF model based on the Doc2Vec algorithm using search keywords and purchase history data of online shopping mall customers. To validate the applicability of the proposed model, we empirically tested its performance using real-world online shopping mall data from Korea. As the number of recommended products increases, the recommendation performance of the proposed CF (or, hybrid CF based on the customer’s search keywords) is improved. On the other hand, the performance of a conventional CF gradually decreased as the number of recommended products increased. As a result, we found that using search keyword data effectively represents customer preferences and might contribute to an improvement in conventional CF recommender systems.