• Journal of Korea Multimedia Society
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• v.21 no.3
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• pp.369-381
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• 2018

Nowadays a personalization algorithm is gaining huge attention. It gives users selective information which is helpful and interesting in a deluge of information based on their past behavior on the internet. However there is also a fatal side effect that the user can only get restricted information on restricted topics selected by the algorithm. Basically, the personalization algorithm makes users have a narrower perspective and even stronger bias because users have less chances to get views of opponent. Eli Pariser called this problem the 'filter bubble' in his book. It is important to understand exactly what a filter bubble is to solve the problem. Therefore, this paper shows how much Google's personalized search algorithm influences search result through an experiment with deep neural networks acting like users. At the beginning of the experiment, two Google accounts are newly created, not to be influenced by the Google's personalized search algorithm. Then the two pure accounts get politically biased by two methods. We periodically calculate the numerical score depending on the character of links and it shows how biased the account is. In conclusion, this paper shows the formation process of filter bubble by a personalization algorithm through the experiment.

• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.3
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• pp.19-29
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• 2010

A marketing model is changed from a customer acquisition to customer retention and it is being moved to a way that enhances the quality of customer interaction to add value to our customers. Such personalization is emerging from this background. The Web site is accelerate the adoption of a personalization, and in contrast to the rapid growth of data, quantitative analytical experience is required. For the automated analysis of large amounts of data and the results must be passed in real time of personalization has been interested in technical problems. A recommendation algorithm is an algorithm for the implementation of personalization, which predict whether the customer preferences and purchasing using the database with new customers interested or likely to purchase. As recommended number of users increases, the algorithm increases recommendation time is the problem. In this paper, to solve this problem, a recommendation system based on clustering and dimensionality reduction is proposed. First, clusters customers with such an orientation, then shrink the dimensions of the relationship between customers to low dimensional space. Because finding neighbors for recommendations is performed at low dimensional space, the computation time is greatly reduced.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.2
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• pp.31-45
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• 2023

This study deals with exploring qualitative steps and paths that appear as YouTube users' usage time increases quantitatively. For the study, I applied theories from psychology and neuroscience, subdivided the interval between the personalization algorithm of the recommendation system, and active use and analyzed the relationship between variables in this process. According to the theory behavioral model theory (FBM), variable reward, and dopamine addiction were applied. Personalization algorithms easy clicks as triggers according to associated content presentation functions in behavioral model theory (FBM). Variable rewards increase motivational effectiveness with unpredictability of the content you search, and dopamine nation is summarized as stimulating the dopaminergic nerve to continuously and actively consume content. This study is expected to make an academic and practical contribution in that it divides the purpose of use of content in the personalization algorithm and active use section into four stages from a psychological perspective: first use, reuse, continuous use, and active use, and analyzes the path.

• Convergence Security Journal
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• v.8 no.4
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• pp.167-179
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• 2008

As ubiquitous technology develops, there are many studies to provide various contents proper to users through a mobile device. However, there is a limit of information provision due to a small user interface of a mobile device. This study proposes a system that can solve a problem and provide an intelligent agent model appropriate to a mobile environment and job information positively that an individual user is interested. It is composed of a personalization engine to monitor users' behavior patterns and a learning algorithm to provide information to a mobile device. Analysis shows that preferred job items are different by sex, age and education, while a region affects job searching significantly.

• Journal of the Korean Society for Library and Information Science
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• v.39 no.3
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• pp.75-91
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• 2005

The popularization of the internet has produced an explosion in amount of the information. The importance of web personalization is being more and more increased. The personalization is realized by learning user's interest. User's interest is changing continuously and rapidly. We use user's profile to represent user's interest. User's profile is updated to reflect the change of user's interest. In this paper we present an adaptive learning algorithm that can be used to reflect user's interest that is changing with time. We propose the User's profile model. With this profile user's interest is learned based on user's feedback. This approach has applied to develop hybrid recommendation system.

• ETRI Journal
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• v.30 no.1
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• pp.101-112
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• 2008

In this paper, we present a method to customize the ground color in outdoor sports video to provide TV viewers with a better viewing experience or subjective satisfaction. This issue, related to content personalization, is becoming critical with the advent of mobile TV and interactive TV. In outdoor sports video, such as soccer video, it is sometimes observed that the ground color is not satisfactory to viewers. In this work, the proposed algorithm is focused on customizing the ground color to deliver a better viewing experience for viewers. The algorithm comprises three modules: ground detection, shot classification, and ground color customization. We customize the ground color by considering the difference between ground colors from both input video and the target ground patch. Experimental results show that the proposed scheme offers useful tools to provide a more comfortable viewing experience and that it is amenable to real-time performance, even in a software-based implementation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09b
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• pp.23-26
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• 2003

In this paper, a methodolgy for customizing web pages for indivisual users is suggested. It shows an efficient way to personalize web pages by predicting one's site access pattern. In addition, the prediction can reflect one's tendency after actual purchase. By using the APRIORI algorithm, one of the association rule search methods, the associativity among the purchase items can be inferred. This inferrence is based on the log data in a web server and database about purchase. Finally, a web page which contains the relationship, relative links on other web pages, and inferred items can be generated after this process.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.5
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• pp.2375-2382
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• 2011

The user clustering for web navigation pattern discovery is very useful to get preference and behavior pattern of users for web pages. In addition, the information by the user clustering is very essential for web personalization or customer grouping. In this paper, an algorithm for clustering the web navigation path of users is proposed and then some special navigation patterns can be recognized by the algorithm. The proposed algorithm has two clustering phases. In the first phase, all paths are classified into k-groups on the bases of the their similarities. The initial solution obtained in the first phase is not global optimum but it gives a good and feasible initial solution for the second phase. In the second phase, the first phase solution is improved by revising the k-means algorithm. In the revised K-means algorithm, grouping the paths is performed by the hyperplane instead of the distance between a path and a group center. Experimental results show that the proposed method is more efficient.

• Journal of Digital Contents Society
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• v.19 no.4
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• pp.701-709
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• 2018

Our research team implemented and operated the system by analyzing the membership information and identifying the different preferences for each group and providing the results of the recommendation based on accumulated membership information and activity log data to the individual. The utilization log was followed up. We analyzed how many people use recommended services and analyzed whether there are any factors other than the personalization service algorithm that affect the service utilization of the system with personalization. In addition, we propose recommendation methods based on behavioral changes when incentives are given through analyzing patterns of users' usage according to methods of recommending services and contents that are often used based on analysis contents.

• Journal of KIISE:Software and Applications
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• v.35 no.8
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• pp.522-528
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• 2008

Topic map typically provide information to user through the selection of topics, that is using only topic, association, occurrence on the first topicmap which is made by domain expert without regard to individual interests or context, for the purpose of supplementation for the weakness which is providing personalized topic map information, personalization has been studied for supporting user preference through preseting of customize, filtering, scope, etc in topic map. Nevertheless, personalization in current topicmap is not enough to user so far. In this paper, we propose a design of PTRS(personalized topicmap ranking system) & algorithm, using both user profile(click through data) and basic element of topic map(topic, association) on knowledge layer in specific domain topicmap, therefore User has strong point that is improvement of personal facilities to user through representation of ranked topicmap information in consideration of user preference using PTRS.