• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2001.01a
• /
• pp.310-315
• /
• 2001

We are focusing on an approach which handle a general Web as a resource in order to support self-directed learning for a student. Then, we are developing a Web based learning environment "Web-Retracer"for utilizing Web as teaching materials by a user′s Annotation. Although the learner can share the Web resource that the others utilized in this environment, Web resources unsuitable for a student′s needs becomes hindrance about her/his self-directed learning. In this paper, we propose a recommending method of the resource united with a student′s needs on the basis of a student′s learning and Web browsing history. This method analyzed the feature peculiar to a resource, and extracts the resource with which the needs of the feature and a student agreed.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.10 no.3
• /
• pp.608-613
• /
• 2006

There are many e-showing mall because of revitalization of e-commerce system. It is necessary to recommending system of products that is for saving time and effort of customer. In this paper, we propose the system that is applying classification among data mining techniques to analysis of log data of customer. This log data contains access of user and purchasing of products. The proposed system operates in two phases. The first phase is composed of data filter module and association extraction module among web pages. The second phase is composed of personalization module and rule generation module. Customer can easily know the recommended sites because the proposed system can present rank of the recommended web pages to customer. As a result, the proposed system can efficiently do recommending of products to customer.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.20 no.8
• /
• pp.1407-1414
• /
• 2016

In this study, we develop that Location Recommending System using personal emotion information based on Collaborative Filtering. Previous Location Recommending System recommended a place visited by the user of the rating or the pattern of location for the user place. These systems are not high user satisfaction because that dose not consider the user status or have not objectively the information. Using user's personal emotion information to recommend a high-affinity users who have visited the place felt similar emotions objectively can improve user satisfaction with the place. In this study, a user using a mobile application directly register the recognized emotion information using the current position and bio-signal, and using the registered information measuring the similarity of user with a similarity emotion, predicts a preference for the place it is recommended to emotional place. The system consists of a user interface, a database, a recommendation module.

• The KIPS Transactions:PartD
• /
• v.11D no.5
• /
• pp.1189-1196
• /
• 2004

It is a major concern of e-shopping mall managers to satisfy a variety of customer's desire by recommending a proper product to the perspective purchaser. Customer information like customer's fondness, age, gender, etc. in shopping has not been used effectively for the customers or the suppliers. Conventionally, e-shopping mall managers have recommended specific items of products to their customers without considering thoroughly in a customer point of view. This study introduces the ways of a choosing and recommending of products using case-based reasoning and rule-based reasoning for customer themselves or others. A similarity measure between one member's idiosyncrasy and the other members' is developed based on the rule base and the case base. The case base is improved for the system intelligence by recognizing and learning the changes of customer's desire and shopping trend.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2003.11a
• /
• pp.121-124
• /
• 2003

It is a major concern of e-shopping mall managers to satisfy a variety of customer's desire by recommending a proper commodity to the expected purchaser. Customer information like customer's fondness and idiosyncrasy in shopping has not been used effectively for the customers or the suppliers. Conventionally, e-shopping mall managers have recommended specific items of commodities to their customers without considering thoroughly in a customer point of view. This study introduces the ways of a choosing and recommending of commodities for customer themselves or others. A similarity measure between one member's idiosyncrasy and the other members' is developed based on the rule base and the case base. The case base is improved by recognizing and learning the changes of customer's desire and shopping trend.

• The Journal of Information Technology and Database
• /
• v.8 no.1
• /
• pp.81-94
• /
• 2001

Providing explanations about recommending actions is one of the most important capabilities of expert systems. In fact, there exist many approaches incorporating this explanation facility into the system. Here we present briefly a new approach to generating these explanations and further attempt to investigate the impact of system explanations on user behaviors toward system-generated recommendations. For this experiment we designed a stock investment decision supporting system which, given a set of market situations, suggests an investment recommendation with explanations about the recommending action. Twenty-nine bank employees evaluated the output of the system in a laboratory setting. The results indicate that explanation facilities can make systems-generated advice more confident to users but cannot increase users'acceptance for the system conclusion.

• The KIPS Transactions:PartD
• /
• v.16D no.2
• /
• pp.169-176
• /
• 2009

Rule discovery is an operation that discovers patterns frequently occurring in a given database. Rule discovery makes it possible to find useful rules from a stock database, thereby recommending buying or selling times to stock investors. In this paper, we discuss storage structures for efficient processing of queries in a system that recommends stock investments. First, we propose five storage structures for efficient recommending of stock investments. Next, we discuss their characteristics, advantages, and disadvantages. Then, we verify their performances by extensive experiments with real-life stock data. The results show that the histogram-based structure improves the query performance of the previous one up to about 170 times.

• Journal of the Korean Institute of Plant Engineering
• /
• v.23 no.4
• /
• pp.57-64
• /
• 2018

User-based collaborative filtering is a method of recommending an item to a user based on the preference of the neighbor users who have similar purchasing history to the target user. User-based collaborative filtering is based on the fact that users are strongly influenced by the opinions of other users with similar interests. Item-based collaborative filtering is a method of recommending an item by comparing the similarity of the user's previously preferred items. In this study, we create a recommendation model using user-based collaborative filtering and item-based collaborative filtering with consumer's consumption data. Collaborative filtering is performed by using RFM (recency, frequency, and monetary) technique with purchasing data to recommend items with high purchase potential. We compared the performance of the recommendation system with the purchase amount and the performance when applying the RFM method. The performance of recommendation system using RFM technique is better.

• Journal of Digital Contents Society
• /
• v.9 no.1
• /
• pp.7-15
• /
• 2008

The paper proposes information system for providing proper well known delicious restaurants as interactions with users. The system calls 'Moloke', which is an agent for recommending sensitive information on mobile environment The proposing agent differs from existing ones that guide the telephone number and the name of the restaurant. The differences are as following goals. First, the agent gets existing from users as interactive communications on mobile devices through the proper requests on each time zone such as morning, afternoon, and evening. Second, the agent also can recommend a specific restaurant for current personal states such as parties, special community meetings, bio-rhythms and so on. Among them, specially the bio-rhythm is used for recommending proper restaurants to each user. In addition to through proposal suitable design for the mobile agent design more effectively. The research used mobile environment for recommendation service and web environment for data management. Server environment for service used Apache, PHP4, Mysql and mobile page was implemented m-html for approach. Mobile Service was optimized Mozilla-1.22, KUN-1.2.3 Browser

• Journal of the Korean BIBLIA Society for library and Information Science
• /
• v.33 no.4
• /
• pp.139-157
• /
• 2022

Finding and selecting proper reviewers is a burden on the publisher of the journal. In order to solve this problem, the online submission system started to recommend appropriate reviewers automatically. It includes a variety of new features, from recommending authors in the references of submitted papers as reviewers to finding similar papers by searching the citation index and suggesting reviewer candidates extensively. This study investigated how the online submission system provides functions such as recommendation of reviewers. As a result of examining major online submission systems, ScholarOne and Editorial Manager were recommending reviewer candidates by commercial citation index and review history platform. On the other hand, JAMS, a domestic online submission system, did not have any advanced functions such as recommendation of candidates for reviewers. Sooner or later, in Korea, it seems that more efforts should be made to improve the function of online submission system, such as recommending suitable reviewers for papers.