• Management & Information Systems Review
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• v.30 no.2
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• pp.81-111
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• 2011

Relationship Marketing has been dealt with as an effective strategy to sustain customer loyalty in many previous researches. For relationship development, a customer's efforts are necessary as well as an organization's efforts. However, the role of customers for the development of the relationship with an organization has been dealt in few previous researches so far. Furthermore, whereas researchers understand the importance of consumers' motivation in the relationship, few researchers had paid attention. This research is based on the Self-Determination Theory (SDT) to explain the role of customer motivation in the process of relationship development and performance. We started by using SDT to confirm the psychological side of relationship development in customer aspects. Then, this paper verified the relationships among environmental factors(informative communication, perceived personalization), relationship motivation(identified motivation, internal motivation) and relational factors(affective commitment, relationship strength). It suggested that customer's roles in psychological parts be inevitable in developing the relationship and it acquired by such stimulations from service providers. In conclusion, this paper has several marketing implications on customer acquisition and retention. For service providers, they should recognize the fact that a customer's perception of self-determination factors can generate tangible and intangible performance in relationship development.

• The Journal of the Korea Contents Association
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• v.8 no.1
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• pp.318-327
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• 2008

With the advance of information technologies and the spread of Internet use, the volume of usable information is increasing explosively. A content recommendation system provides the services of filtering out information that users do not want and recommending useful information. Existing recommendation systems analyze the records and patterns of Web connection and information demanded by users through data mining techniques and provide contents from the service provider's viewpoint. Because it is hard to express information on the users' side such as users' preference and lifestyle, only limited services can be provided. The semantic Web technology can define meaningful relations among data so that information can be collected, processed and applied according to purpose for all objects including images and documents. The present study proposes a content recommendation search system that can update and reflect personalized profiles dynamically in semantic Web environment. A personalized profile is composed of Collector that contains the characteristics of the profile, Aggregator that collects profile data from various collectors, and Resolver that interprets profile collectors specific to profile characteristic. The personalized module helps the content recommendation server make regular synchronization with the personalized profile. Choosing music as a recommended content, we conduct an experience on whether the personalized profile delivers the content to the content recommendation server according to a service scenario and the server provides a recommendation list reflecting the user's preference and lifestyle.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.29-56
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• 2021

Recently, various types of products have been launched with the rapid growth of the e-commerce market. As a result, many users face information overload problems, which is time-consuming in the purchasing decision-making process. Therefore, the importance of a personalized recommendation service that can provide customized products and services to users is emerging. For example, global companies such as Netflix, Amazon, and Google have introduced personalized recommendation services to support users' purchasing decisions. Accordingly, the user's information search cost can reduce which can positively affect the company's sales increase. The existing personalized recommendation service research applied Collaborative Filtering (CF) technique predicts user preference mainly use quantified information. However, the recommendation performance may have decreased if only use quantitative information. To improve the problems of such existing studies, many studies using reviews to enhance recommendation performance. However, reviews contain factors that hinder purchasing decisions, such as advertising content, false comments, meaningless or irrelevant content. When providing recommendation service uses a review that includes these factors can lead to decrease recommendation performance. Therefore, we proposed a novel recommendation methodology through CNN-based review usefulness score prediction to improve these problems. The results show that the proposed methodology has better prediction performance than the recommendation method considering all existing preference ratings. In addition, the results suggest that can enhance the performance of traditional CF when the information on review usefulness reflects in the personalized recommendation service.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.57-71
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• 2013

Over a billion people in the world generate new news minute by minute. People forecasts some news but most news are from unexpected events such as natural disasters, accidents, crimes. People spend much time to watch a huge amount of news delivered from many media because they want to understand what is happening now, to predict what might happen in the near future, and to share and discuss on the news. People make better daily decisions through watching and obtaining useful information from news they saw. However, it is difficult that people choose news suitable to them and obtain useful information from the news because there are so many news media such as portal sites, broadcasters, and most news articles consist of gossipy news and breaking news. User interest changes over time and many people have no interest in outdated news. From this fact, applying users' recent interest to personalized news service is also required in news service. It means that personalized news service should dynamically manage user profiles. In this paper, a content-based news recommendation system is proposed to provide the personalized news service. For a personalized service, user's personal information is requisitely required. Social network service is used to extract user information for personalization service. The proposed system constructs dynamic user profile based on recent user information of Facebook, which is one of social network services. User information contains personal information, recent articles, and Facebook Page information. Facebook Pages are used for businesses, organizations and brands to share their contents and connect with people. Facebook users can add Facebook Page to specify their interest in the Page. The proposed system uses this Page information to create user profile, and to match user preferences to news topics. However, some Pages are not directly matched to news topic because Page deals with individual objects and do not provide topic information suitable to news. Freebase, which is a large collaborative database of well-known people, places, things, is used to match Page to news topic by using hierarchy information of its objects. By using recent Page information and articles of Facebook users, the proposed systems can own dynamic user profile. The generated user profile is used to measure user preferences on news. To generate news profile, news category predefined by news media is used and keywords of news articles are extracted after analysis of news contents including title, category, and scripts. TF-IDF technique, which reflects how important a word is to a document in a corpus, is used to identify keywords of each news article. For user profile and news profile, same format is used to efficiently measure similarity between user preferences and news. The proposed system calculates all similarity values between user profiles and news profiles. Existing methods of similarity calculation in vector space model do not cover synonym, hypernym and hyponym because they only handle given words in vector space model. The proposed system applies WordNet to similarity calculation to overcome the limitation. Top-N news articles, which have high similarity value for a target user, are recommended to the user. To evaluate the proposed news recommendation system, user profiles are generated using Facebook account with participants consent, and we implement a Web crawler to extract news information from PBS, which is non-profit public broadcasting television network in the United States, and construct news profiles. We compare the performance of the proposed method with that of benchmark algorithms. One is a traditional method based on TF-IDF. Another is 6Sub-Vectors method that divides the points to get keywords into six parts. Experimental results demonstrate that the proposed system provide useful news to users by applying user's social network information and WordNet functions, in terms of prediction error of recommended news.

• The Journal of the Korea Contents Association
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• v.14 no.4
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• pp.418-424
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• 2014

This paper aim; to develop the National R&D Information Navigation System(NRnDINS) that is convenient and easy to use by the researchers on the basis of information filtering and visualization by converging and integrating the three types of the contents, namely, paper, report and project at the stage of development of the information system An information system is developed by establishing ontology and RDF on the three types of contents, and by applying information filtering and semantic search technology after having created the prototype for the screen by reflecting the user needs analysis and information visualization elements surveyed at the previous stage of information service planning. In this paper, to make the measure for information filtering, R&D navigation index is prosed and implemented, and NRnDINS capable of integrated search of the R&D contents through information visualization is developed. Also, for the testing of the developed system, the preference survey for its design by 1m persons and usability test of the system by 10 users are performed The result of the survey on the preference for the design is affirmative with 85% of the subjects finding it favorable and the composite receptivity is good with the score of 87.2 the results of the usability test. However, it was also found that further development of the personalization functions is needed. It is hoped that the R&D navigation index of the proposed and implemented in this paper would present quantitative objectivity and will induce further development of other information filtering index of contents in the future.

• Journal of the Korea Convergence Society
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• v.11 no.8
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• pp.23-31
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• 2020

The need for personalized recommendation is growing due to convenient access and various types of items due to the development of information communication and smartphones. Weather and weather conditions have a great influence on the decision-making of users' places and activities. This weather information can increase users' satisfaction with recommendations. In this paper, we propose a collaborative filtering-based place recommendation system using living index by utilizing living index of users' location information on mobile platform to find users with similar propensity and to recommend places by predicting preferences for places. The proposed system consists of a weather module for analyzing and classifying users' weather, a recommendation module using collaborative filtering for place recommendations, and a management module for user preferences and post-management. Experiments have shown that the proposed system is valid in terms of the convergence of collaborative filtering algorithms and living indices and reflecting individual propensity.

• The Journal of the Korea Contents Association
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• v.12 no.12
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• pp.489-498
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• 2012

This paper consists of two parts: In the first part, we describe our work to build hierarchical knowledge base of digital library patron's research interests and learning topics in various scholarly areas through analyzing well classified Electronic Theses and Dissertations (ETDs) of NDLTD Union catalog. Journal articles from ACM Transactions and conference web sites of computing areas also are added in the analysis to specialize computing fields. This hierarchical knowledge base would be a useful tool for many social computing and information service applications, such as personalization, recommender system, text mining, technology opportunity mining, information visualization, and so on. In the second part, we compare four grouping algorithms to select best one for our data mining researches by testing each one with the hierarchical knowledge base we described in the first part. From these two studies, we intent to show traditional verification methods for social community miming researches, based on interviewing and answering questionnaires, which are expensive, slow, and privacy threatening, can be replaced with systematic, consistent, fast, and privacy protecting methods by using our suggested hierarchical knowledge base.

• Journal of Broadcast Engineering
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• v.9 no.4 s.25
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• pp.305-321
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• 2004

With the advent of the digital broadcasting, the audiences can access a large number of TV programs and their information through the multiple channels on various media devices. The access to a large number of TV programs can support a user for many chances with which he/she can sort and select the best one of them. However, the information overload on the user inevitably requires much effort with a lot of patience for finding his/her favorite programs. Therefore, it is useful to provide the persona1ized broadcasting service which assists the user to automatically find his/her favorite programs. As the growing requirements of the TV personalization, we introduce our automatic user preference learning algorithm which 1) analyzes a user's usage history on TV program contents: 2) extracts the user's watching pattern depending on a specific time and day and shows our automatic TV program recommendation system using MPEG-7 MDS (Multimedia Description Scheme: ISO/IEC 15938-5) and 3) automatically calculates the user's preference. For our experimental results, we have used TV audiences' watching history with the ages, genders and viewing times obtained from AC Nielson Korea. From our experimental results, we observed that our proposed algorithm of the automatic user preference learning algorithm based on the Bayesian network can effectively learn the user's preferences accordingly during the course of TV watching periods.

• Journal of Digital Convergence
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• v.20 no.3
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• pp.297-307
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• 2022

This study examined whether there was a difference in the attitude toward the recommended product and the effect on the choice deferral according to information sources. Experiment 1 examined the relationship between trust in information and product attitude, and between uncertainty and choice deferral according to information sources (AI chatbot vs. human). Experiment 2 examined the impact of social presence, perceived personalization, and choice deferral according to whether anthropomorphism of AI chatbots or not. The research results are as follows. First, consumers were found to have a more positive attitude toward products recommended by AI chatbots (vs. human). Second, consumers were more choice deferral whether to purchase products recommended by AI chatbots (vs. human). Third, it was found that consumers' selection of products recommended by anthropomorphic AI chatbots (vs. impersonated AI chatbots) increased. Also, the implications of this study and future research directions were discussed.

• Journal of Internet Computing and Services
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• v.25 no.4
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• pp.23-37
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• 2024

Digital twin is an M&S (Modeling and Simulation) technology designed to solve or optimize problems in the real world by replicating physical objects in the real world as virtual objects in the digital world and predicting phenomena that may occur in the future through simulation. Digital twins have been elaborately designed and utilized based on data collected to achieve specific purposes in large-scale environments such as cities and industrial facilities. In order to apply this digital twin technology to real life and expand it into user-customized service technology, practical but sensitive issues such as personal information protection and personalization of simulations must be resolved. To solve this problem, this paper proposes a federated learning-based accelerated client training method (FACTS) for personalized digital twins. The basic approach is to use a cluster-driven federated learning training procedure to protect personal information while simultaneously selecting a training model similar to the user and training it adaptively. As a result of experiments under various statistically heterogeneous conditions, FACTS was found to be superior to the existing FL method in terms of training speed and resource efficiency.