• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.1
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• pp.88-93
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• 2005

Web usage mining is a research field for searching potentially useful and valuable information from web log file. Web log file is a simple list of pages that users refer. Therefore, it is not easy to analyze user's current interest field from web log file. This paper presents web usage mining method for finding users' current interest based on fuzzy categories. We consider not only how many times a user visits pages but also when he visits. We describe a user's current interest with a fuzzy interest degree to categories. Based on fuzzy categories and fuzzy interest degrees, we also propose a method to cluster users according to their interests for user modeling. For user clustering, we define a category vector space. Experiments show that our method properly reflects the time factor of users' web visiting as well as the users' visit number.

• 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.

• Journal of Internet Computing and Services
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• v.15 no.5
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• pp.73-82
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• 2014

SNS becomes a recent issue, and many researches in various kinds of field are being done by taking advantage of it. Especially, there are many researches existed on the system that finds user's interest and makes recommendation based on multiple social data generated on the SNS. User's interest is not only revealed from the user's writing but also from the user's relationship with friends. This study proposes a recommendation method that extracts user's interest by using social relationship and its categorization applies it to the recommendation. In this way, it can recommend user's interest with category based on the writings by the user and furthermore it can apply the user's relationship with his/her friends for more accurate recommendation. In addition, if necessary, the recommendation can be made by extracting any interest shared between the user and specific friends. Through experiments, we show that our method using social category can produce satisfactory result.

• Journal of Information Processing Systems
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• v.18 no.2
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• pp.245-257
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• 2022

In the Internet of Things (IoT) era, the types of devices used by one user are becoming more diverse and the number of devices is also increasing. However, a forensic investigator is restricted to exploit or collect all the user's devices; there are legal issues (e.g., privacy, jurisdiction) and technical issues (e.g., computing resources, the increase in storage capacity). Therefore, in the digital forensics field, it has been a challenge to acquire information that remains on the devices that could not be collected, by analyzing the seized devices. In this study, we focus on the fact that multiple devices share data through account synchronization of the online platform. We propose a novel way of identifying the user's interest through analyzing the remnants of targeted advertising which is provided based on the visited websites or search terms of logged-in users. We introduce a detailed methodology to pick out the targeted advertising from cache data and infer the user's interest using deep learning. In this process, an improved learning model considering the unique characteristics of advertisement is implemented. The experimental result demonstrates that the proposed method can effectively identify the user interest even though only one device is examined.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.5
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• pp.385-392
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• 2002

Collaborative filtering method for personalization can suggest new items and information which a user hasn t expected. But there are some problems. Not only the steps for calculating similarity value between each user is complex but also it doesn t reflect user s interest dynamically when a user input a query. In this paper, classifying users by their interest makes calculating similarity simple. We propose the a1gorithm for readjusting user s interest dynamically using the profile and Bayesian learning. When a user input a keyword searching for a item, his new interest is readjusted. And the user s profile that consists of used key words and the presence frequency of key words is designed and used to reflect the recent interest of users. Our methods of adjusting user s interest using the profile and Bayesian learning can improve the real satisfaction of users through the experiment with data set, collected in University s library. It recommends a user items which he would be interested in.

• Journal of information and communication convergence engineering
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• v.7 no.2
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• pp.92-97
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• 2009

Lately, influence of information rises and interest about satisfaction estimation of information providing service is risin. According to rapid change in information environment, information-providing service is being changed in various form, in which center development is made in relation to the effort for customer satisfaction intended to enhance user's satisfaction level through providing more convenient and higher service centered on information service user rather than information service provider. Organizations providing information service is also changing their service from traditional one centered on service provider to that for user's satisfaction and service quality, and evaluation of information service quality and measurement of user's satisfaction as the result of using information service are regarded important. In this respect, it is needed to measure user's satisfaction level for environmental factors of information service and analyze what kind of influence they have to enhance user's satisfaction level of information service. Also function and efficiency of information offer service are important. Therefore, interest for satisfaction survey to heighten contents satisfaction of information-providing service, service satisfaction, satisfaction of user of system satisfaction is increased. In this paper, we propose a model of the user satisfaction index for information-providing services and present the user satisfaction index is measured to the model. Also we this study suggest qualitative improvements of information-providing service required for change to user-centric information-providing service through measuring user satisfaction index of ITFIND system and schemes to improve information quality

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.37-54
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• 2010

The information system that preserves and presents information collections, records, processes, retrievals, is applied in various fields recently and is supporting man's many activities. Conventional information systems are based on the reactive interaction model. Such reactive systems respond to only specific instructions, i.e. the defined commands, from the user. To go beyond the reactive interaction, it is necessary that the interactive dynamic interaction based information system which understands human's action and intention autonomously and then provides sensible information adapted to the user. Therefore, we propose a Gaze Mirroring-based intelligent information system for making user's latent interest using the internal state estimation methods based on the interactive dynamic interaction. Then, the proposed Gaze Mirroring method is that an anthropomorphic agent(avatar) actively established the joint attention with the user by imitating user's eye-gaze behavior. We verify that the Gaze Mirroring can elicit the user's behavior reflecting the latent interestand contribute to improving the accuracy of interest estimation. We also have confidence that the Gaze Mirroring promotes the self-awareness of interest. Such a Gaze Mirroring-based intelligent information system also provides suitable information to user by making user's latent interest using the internal state estimation.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.2
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• pp.215-221
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• 2003

Generally, we want to be searched the newest as well as some appropriate personalized information from the internet resources. However, it is a complex and repeated procedure to search some appropriate information. Moreover, because the user's interests are changed as time goes, the real time modeling of a user's interests should be necessary. In this paper, I propose PREA system that can search and filter documents that users are interested from the World Wide Web. And then it constructs the user's interest model by a modified spreading neural network. Based on this network, PREA can easily produce some queries to search web documents, and it ranks them. The conventional spreading neural network does not have a visualization function, so that the users could not know how to be configured his or her interest model by the network. To solve this problem, PREA gives a visualization function being shown how to be made his interest user model to many users.

• The KIPS Transactions:PartC
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• v.13C no.5 s.108
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• pp.651-658
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• 2006

Mobile computing environment is known to be quite difficult to provide user with a stable QoS(Qualify of Service) due to its mobility nature. In order to protect the inherent characteristics of wireless network such as low bandwidth and high transmission delay along with the user's mobility, many works are conducted to apply caching and prefetching methods. This paper presents a novel prefetching technique which is based on user's preference, that is, interest and Uuかity. It tries to improves the effectiveness of prefetching by separating and appling the interest with personal tendency of a given information, and the popularity with general tendency of the information. The proposed scheme shown relatively superior performance in terms of the utilization ratio of prefetched information and the failure ratio of information retrieval than the existing methods.

• Proceedings of the Korea Contents Association Conference
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• 2017.05a
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• pp.453-454
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• 2017

The travellers using social media leave their location history in the form of trajectories. These trajectories can be bridged for acquiring information, required for future recommendation for the future travelers, who are new to that location, providing all sort of information. In this paper, we propose a personalized travel path recommendation scheme based on social life log. By taking advantage of two kinds of social media such as travelogue and community contributed photos, the proposed scheme can not only be personalized to user's travel interest but also be able to recommend a travel path rather than individual Points of Interest (POIs). It also maps both user's and routes' textual descriptions to the topical package space to get user topical package model and route topical package model (i.e., topical interest, cost, time and season).