• Title/Summary/Keyword: User Interest

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User modeling based on fuzzy category and interest for web usage mining

  • Lee, Si-Hun;Lee, Jee-Hyong
    • 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.

A Deep Learning Approach for Identifying User Interest from Targeted Advertising

  • Kim, Wonkyung;Lee, Kukheon;Lee, Sangjin;Jeong, Doowon
    • 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.

A Study on Development of Hybrid Personalization Recommendation System Based on Learing Algorithm (학습알고리즘 기반의 하이브리드 개인화 추천시스템 개발에 관한 연구)

  • Kim Yong;Moon Sung-Been
    • 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.

Social Category based Recommendation Method (소셜 카테고리를 이용한 추천 방법)

  • Yoo, So-Yeop;Jeong, Ok-Ran
    • 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.

Dynamic Recommendation System for a Web Library by Using Cluster Analysis and Bayesian Learning (군집분석과 베이지안 학습을 이용한 웹 도서 동적 추천 시스템)

  • Choi, Jun-Hyeog;Kim, Dae-Su;Rim, Kee-Wook
    • 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.

A Prediction System on User Interest Degree to Web Sites Using the Concept of the Moving Averages (이동평균 개념을 이용한 웹 사이트 사용자 관심도 예측 시스템)

  • 박기현;유상진
    • Korean Management Science Review
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    • v.20 no.1
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    • pp.25-36
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    • 2003
  • Now that many organizations have invested a tremendous amount of money and efforts to operate Web sites on the Internet, there is a strong demand to understand the effectiveness of such investments. In other words, one of most frequent and important questions about their Web sites is "Will the current Web site management policy be effective enough to have more visitors come to our Web site\ulcorner" In this paper, a system which predicts the degree of user interest in the future to Web sites is constructed. The degree of user interest to a Web site is defined to be the visit counts for the Web site in the system. With higher the visit counts, the related site is considered to be more interesting. However, the figures of the visit counts themselves cannot explain properly the degree of user Interest in the future to the related Web sites (i.e. the effectiveness of the related Web sites). Therefore, the system also uses mechanisms which use the concept of the Moving Averages, which have been used frequently in the stock exchanges. In this paper. two prediction mechanisms are proposed and compared. The first mechanism uses the Golden Cross/the Dead Cross of the Moving Averages, while the second mechanism uses the changes of upward/downward direction of the Moving Averages. Experimental results show that the two prediction mechanisms proposed in this paper predict the degree of user interest in the future to the related Web sites very well in most cases. However, the first one is considered to be better than the second one In the sense that the second one is too much sensitive to the changes of visit counts.it counts.

Personalized Agent Modeling by Modified Spreading Neural Network

  • Cho, Young-Im
    • 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.

Gaze Mirroring-based Intelligent Information System for Making User's Latent Interest (사용자의 잠재적 흥미를 인식하기 위한 주시 모방 모델 기반의 지능형 정보 시스템)

  • Park, Hye-Sun;Hirayama, Takatsugu;Matsuyama, Takashi
    • 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.

A Study of user-centric service model and user satisfaction analysis for information service

  • Kim, Chang-Su;Jung, Hoe-Kyung
    • 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

A Dynamic Ontology-based Multi-Agent Context-Awareness User Profile Construction Method for Personalized Information Retrieval

  • Gao, Qian;Cho, Young Im
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.12 no.4
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    • pp.270-276
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    • 2012
  • With the increase in amount of data and information available on the web, there have been high demands on personalized information retrieval services to provide context-aware services for the web users. This paper proposes a novel dynamic multi-agent context-awareness user profile construction method based on ontology to incorporate concepts and properties to model the user profile. This method comprehensively considers the frequency and the specific of the concept in one document and its corresponding domain ontology to construct the user profile, based on which, a fuzzy c-means clustering method is adopted to cluster the user's interest domain, and a dynamic update policy is adopted to continuously consider the change of the users' interest. The simulation result shows that along with the gradual perfection of the our user profile, our proposed system is better than traditional semantic based retrieval system in terms of the Recall Ratio and Precision Ratio.