• The Journal of the Korea Contents Association
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• v.17 no.11
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• pp.183-191
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• 2017

In order to secure the convenience of information retrieval by users of scientific information service platforms and to reduce the time required to acquire the proper information, this study proposes an optimized content recommendation algorithm among the algorithms that currently provide service menus and content information for each service, and conducts comparative evaluation on the results. To enhance the recommendation accuracy, users' major items were added to the original algorithm, and performance evaluations on the recommendation results from the original and optimized algorithms were performed. As a result of this evaluation, we found that the relevance of the content provided to the users through the optimized algorithm was increased by 21.2%. This study proposes a method to shorten the information acquisition time and extend the life cycle of the results as valuable information by automatically computing and providing content suitable for users in the system for each service menu.

• The KIPS Transactions:PartB
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• v.9B no.4
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• pp.389-390
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• 2002

It is important that acquire information about if customer has some habit in electronic commerce application of internet base that led in recommendation service for customer in dynamic web contents supply. Collaborative filtering that has been used as a standard approach to Web personalization can not get rapidly user's preference change due to static user profiles and has shortcomings such as reliance on user ratings, lack of scalability, and poor performance in the high-dimensional data. In order to overcome this drawbacks, Web usage mining has been prevalent. Web usage mining is a technique that discovers patterns from We usage data logged to server. Specially. a technique that discovers Web usage patterns and clusters patterns is used. However, the discovery of patterns using Afriori algorithm creates many useless patterns. In this paper, the enhanced method for the construction of dynamic user profiles using validated Web usage patterns is proposed. First, to discover patterns Apriori is used and in order to create clusters for user profiles, ARHP algorithm is chosen. Before creating clusters using discovered patterns, validation that removes useless patterns by Dempster-Shafer theory is performed. And user profiles are created dynamically based on current user sessions for Web personalization.

• Journal of Information Technology Services
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• v.6 no.2
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• pp.113-123
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• 2007

In ubiquitous computing environment such as ubiquitous marketplace (u-market), there is a need of providing context-based personalization service while considering the nomadic user preference and corresponding requirements. To do so, the recommendation systems should deal with the tremendous amount of context data. Hence, the purpose of this paper is to propose a novel recommendation method which provides the products-group list of the customers in u-market based on the shopping intention and preferences. We have developed FREPIRS(FREquent Purchased Item-sets Recommendation Service), which makes recommendation listof product-group, not individual product. Collaborative filtering and apriori algorithm are adopted in FREPIRS to build product-group.

• Journal of Digital Contents Society
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• v.5 no.3
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• pp.205-212
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• 2004

This paper presents a design and implementation example of intelligent society member management system that is constructed to induce various research activity. Based on members data and society activity record, the system executed data mining. In the process of data mining useful society activity rules was produced and in result members could effectively interact with the system. Decision Tree Algorithm was used in the process, which is one of the methods of data mining. We presemts a plan for personalization website to provide user oriented administration policy and dynamic interface by using analyzed information of society activity rules produced.

• The Journal of Korean Association of Computer Education
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• v.9 no.1
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• pp.71-80
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• 2006

As e-commerce developing rapidly, it is becoming a research focus about how to find customer's behavior patterns and realize commerce intelligence by use of Web mining technology. One of the most successful and widely used technologies for building personalization and goods recommendation system is collaborating filtering. However, collaborative filtering have serious data sparsity problem. Traditional association rule does not consider user's interests or preferences to provide a user with specific personalized service.In this paper, we propose an goods recommendation system, which is integrated an collaborative filtering algorithm with item-to-item corelation and an improved Apriori algorithm. This system has user's interests or preferences ro provide a user with specific personalized service.

• Journal of the Korea Society of Computer and Information
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• v.22 no.12
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• pp.117-123
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• 2017

The personalized search algorithm is a search system that analyzes the user's IP, cookies, log data, and search history to recommend the desired information. As a result, users are isolated in the information frame recommended by the algorithm. This is called 'Filter bubble' phenomenon. Most of the personalized data can be deleted or changed by the user, but data stored in the service provider's server is difficult to access. This study suggests a way to neutralize personalization by keeping on sending random query words. This is to confuse the data accumulated in the server while performing search activities with words that are not related to the user. We have analyzed the rank change of the URL while conducting the search activity with 500 random query words once using the personalized account as the experimental group. To prove the effect, we set up a new account and set it as a control. We then searched the same set of queries with these two accounts, stored the URL data, and scored the rank variation. The URLs ranked on the upper page are weighted more than the lower-ranked URLs. At the beginning of the experiment, the difference between the scores of the two accounts was insignificant. As experiments continue, the number of random query words accumulated in the server increases and results show meaningful difference.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.28 no.4
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• pp.501-518
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• 2017

Reducing the time it takes for researchers to acquire knowledge and introduce them into research activities can be regarded as an indispensable factor in improving the productivity of research. The purpose of this research is to cluster the information usage patterns of KOSEN users and to suggest optimization method of personalized recommendation service algorithm for grouped users. Based on user research activities and usage information, after identifying appropriate services and contents, we applied a Spark based big data analysis technology to derive a personal recommendation algorithm. Individual recommendation algorithms can save time to search for user information and can help to find appropriate information.

• The Journal of the Korea Contents Association
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• v.10 no.11
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• pp.461-467
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• 2010

In this paper, the framework of a personalized search system designing and can perform features which are suitable for each contents by using various contents a feature was developed. In addition, by using various contents including the domestic paper, patent, trend information, and etc., it applied to the various algorithm of the personalized search system.

• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.1
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• pp.21-31
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• 2020

Internet of Things is growing rapidly. As it evolves, the amount of data is increasing significantly. It requires a new personalized information retrieval method. Internet of Things is defined as uniquely identifiable interoperable connected object. The first definition of Internet of Things was from Things oriented perspective. However, previous studies about personalized information retrieval method do not consider Things. To meet user's individual needs, previous studies concentrate on only human, not Things. In this paper, we propose a personalized information retrieval method considering participating device in Internet of Things. It provides personalized information using data type preference for each device. Moreover, it provides personalized results by integrating data type preference for set of devices. This paper describes a new personalized retrieval method and algorithm. It consists of five steps. Then, it presents four scenarios using proposed method. The scenarios show our work is more effective and efficient than existing one.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2003.05a
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• pp.416-425
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• 2003

Recommender systems are a personalized information filtering technology to help customers find the products they would like to purchase. Collaborative filtering (CF) has been known to be the most successful recommendation technology. However its widespread use in e-commerce has exposed two research issues, sparsity and scalability. In this paper, we propose several hybrid recommender procedures based on web usage mining, clustering techniques and collaborative filtering to address these issues. Experimental evaluation of suggested procedures on real e-commerce data shows interesting relation between characteristics of procedures and diverse situations.