• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.93-97
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• 2002

In this paper, we propose personalized recommendation techniques based on multidimensional scaling (MDS) method for Business to Consumer Electronic Commerce. The multidimensional scaling method is traditionally used in marketing domain for analyzing customers' perceptional differences about brands and products. In this study, using purchase history data, customers in learning dataset are assigned to specific product categories, and after then using MDS a positioning map is generated to map product categories and alternative advertisements. The positioning map will be used to select personalized advertisement in real time situation. In this paper, we suggest the detail design of personalized recommendation method using MDS and compare with other approaches (random approach, collaborative filtering, and TOP3 approach)

• Journal of Information Technology Application
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• v.3 no.1
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• pp.1-21
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• 2001

The explosive growth of the Internet and the increasing popularity of the World Wide Web have generated significant interest in the development of electronic commerce in a global online marketplace. The rapid adoption of the Internet as a commercial medium is rapidly expanding the necessity of Web advertisement as a new communication channel. if proper Web advertisement could be suggested to the right user, then effectiveness of Web advertisement will be raised and it will help company to earn more profit. So, this article describes a personalized advertisement technique as a part of intelligent customer services for an electronic newspaper provide. Based on customers history of navigation on the electronic newspapers pages, which are divided into several sections such as politics, economics, sports, culture, and so on, appropriate advertisements (especially, banner ads) are chosen and displayed with the aid of machine learning techniques, when customers visit to the site. To verify feasibility of the technique, an application will be made to one of the most popular e-newspaper publishing company in Korea.

• Journal of Digital Contents Society
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• v.11 no.1
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• pp.105-115
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• 2010

Search engines provide web documents that are related to user's query. However, using only the query terms that user provided, it is hard for search engines to know user's exact intention and provide the very matching web documents. To remedy this problem, search systems are needed to exploit personalized search technologies. In this paper, we propose not only a novel personalized query recommendation scheme based on folksonomy but also a new personalized search service architecture which reduces the risk of privacy violation while enabling search service providers to provide other various personalized services such as personalized advertisement.

• Information Systems Review
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• v.17 no.2
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• pp.77-100
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• 2015

This study examined the factors that influence the smartphone user's decision to accept the personalized mobile advertisement. As a theoretical basis, we applied the privacy calculus model (PCM) that illustrates how consumers are engaged in a dynamic adjustment process in which privacy risks are weighted against benefits of information disclosure. In particular, we investigated how smartphone users make a risk-benefit assessment under which personalized service as benefit-side factor and information privacy risks as a risk-side factor accompanying their acceptance of advertisements. Further, we extend the current PCM by considering advertisement fatigue as a new factor that may influence the user's acceptance. The research model with five (5) hypotheses was tested using data gathered from 215 respondents through a quasi-experimental survey method. During the survey, each participant was asked to navigate the website where the experimental simulation of a mobile advertisement service was provided. The results showed that three (3) out of five (5) hypotheses were supported. First, we found that the intention to accept advertisements is positively and significantly influenced by the perceived value of personalization. Second, perceived advertisement fatigue was also found to be a strong predictor of the intention to accept advertisements. However, we did not find any evidence of direct influence of privacy risks. Finally, we found that the significant moderating effect between the perceived value of personalization and advertisement fatigue. This suggests that the firms should provide effective tailored advertisement that can increase the perceived value of personalization to mitigate the negative impacts of advertisement fatigue.

• Korean Management Science Review
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• v.17 no.3
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• pp.19-30
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• 2000

Customization and personalization services are considered as a critical success factor to be a successful Internet store or web service provider. As a representative personalization technique, personalized recommendation techniques are studied and commercialized to suggest products or services to a customer of Internet storefronts based on demographics of the customer or based on an analysis of the past purchasing behavior of the customer. The underlining theories of recommendation techniques are statistics, data mining, artificial intelligence, and/or rule-based matching. In the rule-based approach for personalized recommendation, marketing rules for personalization are usually collected from marketing experts and are used to inference with customers data. however, it is difficult to extract marketing rules from marketing experts, and also difficult to validate and to maintain the constructed knowledge base. In this paper, we proposed a marketing rule extraction technique for personalized recommendation on Internet storefronts using market basket analysis technique, a well-known data mining technique. Using marketing basket analysis technique, marketing rules for cross sales are extracted, and are used to provide personalized advertisement selection when a customer visits in an Internet store. An experiment has been performed to evaluate the effectiveness of proposed approach comparing with preference scoring approach and random selection.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2004.11a
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• pp.137-141
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• 2004

본 논문은 디지털방송 컨텐츠(드라마, 영화, 토크쇼)상에서 PPL(Product Placement) 간접광고를 보다 개인화 된 맞춤 광고로 구현한 어플리케이션을 기술한다. 이러한 애플리케이션은 개인의 취향에 최적화된 광고를 제공하고 방송사와 시청자간의 Interaction에 의해 전자상거래가 가능한 채널로 이동할 수 있는 기능을 제공한다. 다시 말해서 본 논문의 어플리케이션은 컨텐츠 시작 전에 개인이 선호하는 물품을 선택하여 컨텐츠 속에 나오는 PPL광고에서 시청자가 선택한 물품만이 컨텐츠 방영 중에 나타나고, 그 선택 물품의 상세 정보와 구매를 할 수 있는 DAL(Dedicated Advertisers Location)채널로 이동할 수 있도록 하였다. 따라서 시청자 측면에서는 개인화 된 방송 서비스를 이용하여 자신이 원하는 선별된 광고를 보는 효율적이고 능동적인 방송시청을 하게 되며, 방송 사업자 측면에서는 맞춤 방송 서비스로 효과적인 타겟 소비자를 정하여 효과적인 마케팅을 할 수 있다. 그리고 시청한 광고 물품들을 장바구니라는 일종의 북마크에 담을 수 있게 하였다. 시청자가 원할 때는 언제든지 광고된 물품의 T-Commerce채널로 이동 가능하도록 설계, 구현하였다. 이것은 개인화 된 맞춤형 방송과 쌍방향 Interaction이 가능한 새로운 데이터방송의 특성을 잘 보여주는 Interactive 광고로서 새로운 모델이 될 것이다. 본 논문의 어플리케이션(Xlet)은 우리나라 위성방송 데이터방송 표준인 MHP 미들웨어에 의해 구동되어지며, 데이터방송용 API인 JavaTV API, Havi & Davic API에 따라 구현되어졌다.

• Journal of Digital Convergence
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• v.17 no.2
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• pp.81-89
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• 2019

The purpose of this paper was to investigated the effect of the constituents of Facebook advertisement on ad fitness recognition and WOM intension. In addition, we confirmed the mediating effect of ad fitness recognition. For this purpose, data were collected for Facebook 371 users. The research method used factor analysis and covariance structure analysis. The results of this study were as follows: First, the advertising interest characteristics of Facebook ads had a significant effect on ad fitness and word-of-mouth intension. Second, personalized fit informational attributes of Facebook ads had a significant effect on ad fitness and WOM intension. Third, the perceived responsiveness had a significant effect on perceived fitness of advertisement and WOM intention. Fourth, perceived fitness recognition of Facebook advertisement had a significant effect on WOM intension. The results of this study provide consumers with information on what to consider when creating an identity advertisement and effective advertisement as Facebook advertisement.

• Journal of the Korea Computer Industry Society
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• v.2 no.9
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• pp.1175-1182
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• 2001

One to one Marketing (a.k.a. database marketing or relationship marketing) is one of the many fields that will benefit from the electronic revolution and shifts in consumer sales and advertising. As a component of intelligent customer services on Internet storefront, this paper describes technology of providing personalized advertisement using the market basket analysis, a well-Known data mining technique. The underlining theories of recommendation techniques are statistics, data mining, artificial intelligence, and/or rule-based matching. In the rule-based approach for personalized recommendation, marketing rules for personalization are usually collected from marketing experts and are used to inference with customer's data. However, it is difficult to extract marketing rules from marketing experts, and also difficult to validate and to maintain the constructed Knowledge base. In this paper, using marketing basket analysis technique, marketing rules for cross sales are extracted, and are used to provide personalized advertisement selection when a customer visits in an Internet store.

• Asia pacific journal of information systems
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• v.22 no.4
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• pp.125-149
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• 2012

As the smart phone is propagating rapidly, the importance of mobile advertisement has also grown. One of the main characteristics of the Internet and smart phone advertising is that they can deliver personalized advertisements to each customer. The smart phone enables the identification of additional personalized information such as the customer's location and the accessibility to the site at any place any time. As the Internet platform becomes richer, firms that offer the ad services via the wired PC Internet and wireless smart phone are seeking various types of personalized ads. However, their service platform and Information and Communication Technology (ICT) platform should be suitable to the characteristics of personalized ads. This research explores various types of personalized ad methods and evaluates their adequacy encompassing four types of ad service platforms (such as search portal, news portal, e-mall servers, and SNS) and two types of ICT platforms (PC Internet and smart phone). To this end, we classified the personalized ads into seven types: three basic types and four composite types. The basic types of ad methods are identified by considering the current activity that the customer is engaged, the individual profile and log history, and the customer's current location or planning location. Four composite types of ad methods are constructed as the combination of these basic types. For those types of ad methods, we evaluate whether each ad method adequately maps with four types of ad service platforms and two types of ICT platforms. We proposed a metric of evaluation and demonstrated the concept with illustrative numbers. Specifically, we analyze and compare personalized ad methods in three ways. Firstly, the possibility of implementing a personalized ad method on the platform is analyzed to confirm the degree of suitability. Secondly, the value of personalized ad method is analyzed based on the customer accessibility. Lastly, expected effectiveness for each personalized ad method is computed by multiplying the possibility and the value. Through this kind of analysis, the ad service providers as well as advertising companies can evaluate what kinds of personalized ad methods and platforms are possible and suitable to maximize their ad effectiveness on the Internet and smart phone platforms.

• The Transactions of the Korea Information Processing Society
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• v.6 no.6
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• pp.1590-1597
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• 1999

Advertisements on the Web, rising as a major profit source of Web services, have a distinctive characteristic of detailed classification of potential customers, compared with those of other conventional media such as TV and newspaper. It is therefore possible to advertise selectively according to personal characteristics and to record precise advertisement effects. Web-based advertisement management systems of nowadays have the capability to select ones compatible with personal environment characteristics and registered information, and to provide processed information and knowledge about advertisement effects based on usage recordings. However, they have severe problems in modeling diverse requirements or characteristics of users : customers, advertisers and ISP, and in matching and coordinating of them. In order to solve these problems, we propose a frame work for coordinating the needs of users, advertisers, and ISPs, which is built on top of tree-style classification of advertisements. Other schemes are supported around the framework as follows : (1) characteristics management of pages within themselves, (2) rule-based modeling of advertisement target, and (3) user modeling and case-based analysis. We propose a prototype system within the framework.