• 디지털콘텐츠학회 논문지
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• 제19권1호
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• pp.85-91
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• 2018

대부분의 은행은 고객 세분화를 위해 성별, 나이, 직업, 주소 등 인구통계정보만을 사용하고 있으나, 이는 고객의 다양한 금융행동 패턴을 반영하지 못하는 단점이 있다. 본 연구에서는 은행 내 다양한 빅데이터를 융합하여 문제점을 해결함과 동시에 향후 많은 은행에서 폭넓게 활용될 수 있는 고객 세분화 방법을 개발하는 것을 목표로 한다. 본 연구에서 제안한 블록을 만들어 이 블록을 클러스터링하는 상향식 방식의 세분화는 기법을 제안한다. 이 방식은 기존의 인구통계정보 뿐만 아니라 다양한 거래패턴, 채널접촉패턴에 기반을 둔 고객의 다양한 금융니즈를 정교하게 반영할 수 있다는 장점이 있다. 세분화를 통해 고객의 금융니즈를 보다 정교하게 반영한 적정 동료그룹을 찾아 이를 기반으로 상품추천, 금융니즈 등급 산출, 고객이탈 예측 등 다양한 마케팅 모델을 개발하여 실제 농협은행 마케팅에 활용할 것이다.

• 지능정보연구
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• 제17권3호
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• pp.115-130
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• 2011

온라인 스토어들은 다양한 방식으로 사용자들에게 신뢰감을 가져다 줄 수 있는 요인들을 제공하려고 한다. 대표적인 방식이 고객이 좋아할 만한 제품의 추천과 고객제품리뷰의 제공이다. 각각의 제공을 통해 신뢰의 선행요인이 되는 사회적 실재감을 향상시킬 수 있다는 연구들이 있어왔다. 따라서 본 연구에서는 추천 상황에 따른 사회적 실재감에 미치는 영향과 추천 상황과 제품군의 유형, 고객제품리뷰의 제공여부에 따라 사회적 실재감의 증가에 미치는 영향을 실험을 통해 분석하였다. 개인화 추천을 통해 사회적 실재감을 증대시킬 수 있었으며, 쾌락재에서는 고객제품리뷰의 제공을 통해 어떤 추천 상황에서든 사회적 실재감이 증대되나 유의한 차이를 보이지는 않았다.

• Asia pacific journal of information systems
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• 제31권4호
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• pp.472-490
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• 2021

The detection of products with manipulated reviews has received widespread research attention, given that a truthful, informative, and useful review helps to significantly lower the search effort and cost for potential customers. This study proposes a method to recognize products with manipulated online customer reviews by examining the sequence of each review's sentiment, readability, and rating scores by product on randomness, considering the example of a Russian online retail site. Additionally, this study aims to examine the association between brand awareness and existing manipulation with products' reviews. Therefore, we investigated the difference between well-known and unknown brands' products online reviews with and without manipulated reviews based on the average star rating and the extremely positive sentiment scores. Consequently, machine learning techniques for predicting products are tested with manipulated reviews to determine a more useful one. It was found that about 20% of all product reviews are manipulated. Among the products with manipulated reviews, 44% are products of well-known brands, and 56% from unknown brands, with the highest prediction performance on deep neural network.

• 벤처창업연구
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• 제11권2호
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• pp.157-165
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• 2016

이 논문은 미시(微視)기반레벨에서 (at Micro-Foundations level) PBR(등급에 의한 가격책정)도구를 개발하여 PBR이 어느 상황에서든 가격책정(Pricing)의 기준과 비즈니스모델평가에 대한 일반적인 기준으로 사용될 수 있음을 주장하고 있다. 본 논문은 우선 구매력과 지불/구매의향 (Willingness to Pay/Purchase: WTP)을 동시에 지니고 있는 현시니즈(Explicit Needs)로부터 WTP모델을 유도하여 WTP수준과 WTS(willingness to supply/sell: 공급/판매의향) 수준간의 간격에 대한 서열척도(ordinal scale)를 취하여 PBR방법을 개발하였다. 구체적으로 고객이 기대하는 이상적 마케팅믹스인 최선의 SPEC (Solution, Price Indicator by WTP, Encouragement, Channel)과 기업이 제공하는 실제 마케팅믹스 (Marketing Mix) 4P에 대하여 우선 각 구성요소 마다마다를 상호 개별적으로 비교할 뿐만 아니라 전체를 하나로 인식하여 상호 비교함으로써 PBR방법을 개발한 후 이를 적용한 몇 가지 예시를 통해서 PBR방법이 실제로 비즈니스모델을 평가하는데 사용될 수 있음을 보여 준다. 결론적으로 본 논문은 어떤 상황에서든 PBR이 가격책정과 비즈니스모델의 평가도구로서 유용하게 사용될 수 있다고 주장한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권2호
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• pp.538-561
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• 2020

Recommender Systems (RecSys) have a major role in e-commerce for recommending products, which they may like for every user and thus improve their business aspects. Although many types of RecSyss are there in the research field, the state of the art RecSys has focused on finding the user similarity based on sequence (e.g. purchase history, movie-watching history) analyzing and prediction techniques like Recurrent Neural Network in Deep learning. That is RecSys has considered as a sequence prediction problem. However, evaluation of similarities among the customers is challenging while considering temporal aspects, context and multi-component ratings of the item-records in the customer sequences. For addressing this issue, we are proposing a Deep Learning based model which learns customer similarity directly from the sequence to sequence similarity as well as item to item similarity by considering all features of the item, contexts, and rating components using Dynamic Temporal Warping(DTW) distance measure for dynamic temporal matching and 2D-GRU (Two Dimensional-Gated Recurrent Unit) architecture. This will overcome the limitation of non-linearity in the time dimension while measuring the similarity, and the find patterns more accurately and speedily from temporal and spatial contexts. Experiment on the real world movie data set LDOS-CoMoDa demonstrates the efficacy and promising utility of the proposed personalized RecSys architecture.

• 한국조리학회지
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• 제12권2호
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• pp.1-17
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• 2006

This study is to develop a viable measurement tool of crowdedness in restaurants. First, to measure customer’s awareness of crowdedness related to each different environmental factor in a restaurant, 49 factors of crowdedness awareness were selected based on the previous studies and then properties of each factor affecting customer’s satisfaction for crowdedness were analyzed. To analyze effects of each factor upon the satisfaction rate, a Multiple Regression Analysis was conducted with the crowdedness awareness as an independent variable and satisfaction of crowdedness as a dependent variable. The results showed that when an analysis of the environmental factors in the crowdedness awareness was conducted in an effort to develop a measurement tool of crowdedness awareness in restaurants, that would be fit for the domestic food service market, based upon 49 factors of restaurant environment. Focusing on expectation and satisfaction rate, it was found that rating the satisfaction level would be a much more effective tool to measure crowdedness awareness because satisfaction rate appeared to be more closely related to the awareness than the results obtained from the Multiple Regression Analysis with a difference between expectation and satisfaction scores as an independent variable.

• Industrial Engineering and Management Systems
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• 제7권3호
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• pp.214-227
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• 2008

Today's dynamic competitiveness requires an organization to improve its performance measurement and management. Quality Management Systems (QMS) abound, the main ones being: ISO series, Malcolm Baldridge National Quality Award (MBNQA), European Forum for Quality Management (EFQM), Six Sigma Business Scorecard and the Balanced Scorecard. Based on the literature, the IPMMM (Integrated Performance Measurement and Management Model) identified 7 key synthesized factors: leadership, strategy management and policy, customer and market, learning and growth, partnership and resources, internal processes and business results that are employed to investigate the key performance indicators of a car assembler using the Delphi methodology. In the 2 rounds of Delphi panels consisting of 20 senior management personnel, the $1^{st}$ round of 198 indicators in the IPMMM yielded 90 indicators. The $2^{nd}$ round yielded 43 performance indicators with 18 rated as critical based on the % assigned in the $1^{st}$ and $2^{nd}$ priority rating of "very important factor" and "key performance indicator" that must be ranked high on both of the priorities. The very critical indicators appeared to be: defect percentage and first time capability (tie in $1^{st}$ place) and revenue, goal setting, customer satisfaction index, on-time delivery, brand image, return on investment, Claim Occurrence Ratio, and debt being ranked from $3^{rd}$ to $10^{th}$. It can be surmised that an organization can identify and develop an appropriate set of performance indicators through the Delphi methodology and implement and manage them based on the Balanced Scorecard.

• 한국정보과학회 언어공학연구회:학술대회논문집(한글 및 한국어 정보처리)
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• 한국정보과학회언어공학연구회 2018년도 제30회 한글 및 한국어 정보처리 학술대회
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• pp.652-657
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• 2018

With the rapid development of e-commerce, many customers can now express their opinion on various kinds of product at discussion groups, merchant sites, social networks, etc. Discerning a consensus opinion about a product sold online is difficult due to more and more reviews become available on the internet. Opinion Mining, also known as Sentiment analysis, is the task of automatically detecting and understanding the sentimental expressions about a product from customer textual reviews. Recently, researchers have proposed various approaches for evaluation in sentiment mining by applying several techniques for document, sentence and aspect level. Aspect-based sentiment analysis is getting widely interesting of researchers; however, more complex algorithms are needed to address this issue precisely with larger corpora. This paper introduces an approach of knowledge representation for the task of analyzing product aspect rating. We focus on how to form the nature of sentiment representation from textual opinion by utilizing the representation learning methods which include word embedding and compositional vector models. Our experiment is performed on a dataset of reviews from electronic domain and the obtained result show that the proposed system achieved outstanding methods in previous studies.

• 한국IT서비스학회지
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• 제13권3호
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• pp.309-321
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• 2014

Recently, due to the advent of ubiquitous computing and the spread of intelligent portable device such as smart phone, iPad and PDA has been amplified, a variety of services and the amount of information has also increased fastly. It is becoming a part of our common life style that the demands for enjoying the wireless internet are increasing anytime or anyplace without any restriction of time and place. And also, the demands for e-commerce and many different items on e-commerce and interesting of associated items are increasing. Existing collaborative filtering (CF), explicit method, can not only reflect exact attributes of item, but also still has the problem of sparsity and scalability, though it has been practically used to improve these defects. In this paper, using a implicit method without onerous question and answer to the users, not used user's profile for rating to reduce customers' searching effort to find out the items with high purchasability, it is necessary for us to analyse the segmentation of customer and item based on customer data and purchase history data, which is able to reflect the attributes of the item in order to improve the accuracy of recommendation. We propose the method of recommendation system using association rule and weighted preference so as to consider many different items on e-commerce and to refect the profit/weight/importance of attributed of a item. To verify improved performance of proposing system, we make experiments with dataset collected in a cosmetic internet shopping mall.

• The Journal of Asian Finance, Economics and Business
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• 제7권7호
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• pp.279-291
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• 2020

This study aims to develop the concept of innovation models with the marketing channel construct approach, marketing innovation, product segmentation, and customer insight; as well as improvements to the theory of resource-based combined with the method of service-dominant logic. This study approach is based on quantitative descriptive conducted with three stages of testing scenarios. The first test is the mapping of the innovation model construct through testing the validity and reliability with the moderation of customer orientation variables. The second scenario examines the relationship of influence between the independent variables on the dependent variable of 29 hypothetical analysis equation modeling. The unit of analysis was conducted on 497 SMEs involved in the food and beverage sectors, with the criteria being SMEs must have a rating of 4-5 points on the Go-Food applications software. The results shown that: 1) the construct used to develop an innovative model both directly and via moderation is positive and significant; 2) Through a complicated relationship that involves all components of the variable, it outlines a positive and significant effect except for the path of analysis (μ5). The theoretical and managerial implications state that the service-dominant logic approach and resource-based view theory have extreme reliability and interrelations.