• Journal of KIISE:Software and Applications
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• v.27 no.3
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• pp.248-256
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• 2000

This paper presents a personalized web agent that constructs user profile which consists of user preferences on the web and recommends his/her relevant information to the user. The personalized web agent consists of monitor agent, user profile construction agent, and user profile refinement agent. The monitor agent makes a user describe his/her preferences directly and it creates the database of preference document, finally performs several keyword extraction to increase the accuracy of the DB. The user profile construction agent transforms the extracted keywords into user profile that could be confirmed and edited by the user. and the refinement agent refines user profile by recursively learning and processing user feedback. In this paper, we describe the several keyword weighting and inductive learning techniques in detail. Finally, we describe the adaptive web retrieval and push agent that perform adaptive services to the user.

• Journal of KIISE:Software and Applications
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• v.31 no.4
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• pp.439-446
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• 2004

To design a user-adaptive web surfing system, we nay take the approach to divide the whole process into three phases; collecting user data, processing the data to construct and improve the user profile, and adapting to the user by applying the user profile. We have designed three software agents. Each privately works in each phase and they collaboratively support adaptive web surfing. They are IIA(Interactive Interface Agent), UPA(User Profile Agent), and ANA(Autonomous Navigation Agent). IIA provides the user interface, which collects data and performs mechanical navigation support. UPA processes the collected user data to build and update the user profile while user is web-surfing. ANA provides an autonomous navigation mode in which it automatically recommends web pages that are selected based on the user profile. The proposed approach and design method, through extensions and refinements, may be used to build a practical adaptive web surfing system.

• 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.

• The KIPS Transactions:PartB
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• v.9B no.4
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• pp.399-406
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• 2002

The goal of this research has been to develop an adaptive user agent for web surfing. To achieve this goal, the research has concentrated on three issues: collection of user data, construction and improvement of user profile, and adaptation by applying the user profile. The main outcome from the research is a prototype system that provides the functional definition and componential design scheme for an adaptive user agent for the web environment. Internally, the system achieves its operational goal from the cooperation of two independent agents. They are IIA (Interactive Interface Agent) and UPA (User Profiling Agent). As a tool for providing a user-friendly interface environment, the IIA employs the Keyword Index, which is a list of index terms of a webpage as well as a keyword menu for subsequent queries, and the Suggest Link, which is a hierarchical list of URLs showing the past browsing procedure of the user. The UPA reflects in the User Profile, both the static and the dynamic information obtained from the user's browsing behavior. In particular, a user's interests are represented in the form of Interest Vectors which, based on the similarity of the vectors, is subject to update and creation, thus dynamically profiling the user's ever-shifting interests.

• The KIPS Transactions:PartD
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• v.11D no.5
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• pp.1205-1212
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• 2004

As electronic commerce systems have been widely used, the necessity of adaptive e-commerce agent systems has been increased. These kinds of adaptive e-commerce agents can monitor customer's behaviors and cluster thou in similar categories, and include user's preference from each category. In order to implement our adaptive e-commerce agent system, in this paper, we propose an adaptive e-commerce agent systems consider customer's information of interest and goodwill ratio about preference goods. Proposed system build user's profile more accurately to get adaptability for user's behavior of buying and provide useful product information without inefficient searching based on such user's profile. The proposed system composed with three parts , Monitor Agent which grasps user's intension using monitoring, similarity reference Agent which refers to similar group of behavior pattern after teamed behavior pattern of user, Interest Analyzing Agent which personalized behavior DB as a change of user's behavior.

• Journal of KIISE:Software and Applications
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• v.33 no.12
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• pp.1062-1072
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• 2006

Recently, new intelligent service frameworks, such as ubiquitous computing are proposed. So, the necessity of adaptive agent system has been increased. In this paper, we propose an intelligent service agent to help that ubiquitous computing system offer user suitable service in ubiquitous computing environment. In order to offer user suitable uT-service, an intelligent service agent mediates the gap between the context information in uT-service system, and user preference is reflected in it. Therefore, we focus on following three components; the first is suitable multi agent framework-agent communication analysis and applicable method of inference engine, the second is uT-ontologies to describe various context information-context information sharing between agents and context information understanding between agents, the third is learning method of user profile to apply in uT-service system. This approach enables us to build adaptive uT-service system to offer suitable service according to user preference.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.407-410
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• 1999

WWW is an important method for retrieving or providing informations. Not only the amount of information but also it is widely located on the web, it is difficult for users to get or search information. Furthermore, to use search engine is also inconvenient, because it just uses a keyword without concerning a user's interest. At this point, we propose a design of web agent that uses the automatic document categorization system and user's profile concerning with a user's interest, so the agent can actively provide a information.

• The KIPS Transactions:PartB
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• v.9B no.6
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• pp.775-782
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• 2002

As electronic commerce systems have been widely used, the necessity of adaptive e-commerce agent systems has been increased. These kinds of agents can monitor customer's purchasing behaviors, clutter them in similar categories, and induce customer's preference from each category. In order to implement our adaptive e-commerce agent system, we focus on following 3 components-the monitor agent which can monitor customer's browsing/purchasing data and abstract them, the conceptual cluster agent which cluster customer's abstract data, and the customer profile agent which generate profile from cluster, In order to infer more accurate customer's preference, we propose a 2 layered structure consisting of conceptual cluster and inductive profile generator. Many systems have been suffered from errors in deriving user profiles by using a single structure. However, our proposed 2 layered structure enables us to improve the qualify of user profile by clustering user purchasing behavior in advance. This approach enables us to build more user adaptive e-commerce system according to user purchasing behavior.

• The KIPS Transactions:PartB
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• v.8B no.5
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• pp.485-492
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• 2001

Recently, character agents are used as a user-friendly interface. In this paper, we have studied a generic framework for emotional character agents which are designed to infer emotions from diverse personalities, situations, user behaviors and to express them. The method of emotion inference is based on blackboard systems which are used to solve the problems in AI. Because it keeps independence between knowledge sources which are rules of emotions, a blackboard-based inference engine is easy to manage knowledge sources, Blackboard-based systems gave the system flexibility. So we can adapt the engine to various application systems. Each emotional agent monitors user behavior, learns user profile and infers user behavior. And it generates characters emotions according to the user profile. So, in case of same situations, the agent can generate different emotions according to users. We have studied to build an personalized emotional character agent which according to situations and user modeling.

• Journal of the Korea Society of Computer and Information
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• v.20 no.1
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• pp.29-37
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• 2015

Nowadays, the user-centric application based web 2.0 has replaced the web 1.0. The users gain and provide information by interactive network applications. As a result, traditional approaches that only extract and analyze users' local document operating behavior and network browsing behavior to build the users' preference profile cannot fully reflect their interests. Therefore this paper proposed a preference analysis and indicating approach based on the users' communication information from MicroBlog, such as reading, forwarding and @ behavior, and using the improved PersonalRank method to analyze the importance of a user to other users in the network and based on the users' communication behavior to update the weight of the items in the user preference. Simulation result shows that our proposed method outperforms the ontology model, TREC model, and the category model in terms of 11SPR value.