• The Journal of the Convergence on Culture Technology
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• v.7 no.1
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• pp.595-607
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• 2021

In response to increasing demand of contactless services, the overall usage of "task-oriented chatbots" in the industry is on the rise. The purpose of a task-oriented chatbot is to raise the efficiency of data sharing and workflow; in order to establish a guideline, there must be a discussion on "what" and "how" to share information. We investigate the effects of personalization and different types of the interface on 'performance expectancy', 'effort expectancy', 'intention to use', and 'satisfaction' in the context of a task-oriented chatbot. Results show that 'intention to use' and 'satisfaction' were higher when the level of personalization was higher. Within the closed-discourse interface, 'intention to use' and 'satisfaction' were higher when personalization was lower. We highlight the practical insights in the use of personalization and types of chatbot interface based on 'perceived personalization', 'expectation disconfirmation theory', 'privacy concern' and 'privacy paradox'.

• Journal of Computing Science and Engineering
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• v.6 no.4
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• pp.243-256
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• 2012

In this paper we present a framework for a novel kind of context-aware preference query composition whereby queries for the Preference SQL system are created. We choose a commercial e-business platform for outdoor activities as a use case and develop a context model for this domain within our framework. The suggested model considers explicit user input, domain-specific knowledge, contextual knowledge and location-based sensor data in a comprehensive approach. Aside from the theoretical background of preferences, the optimization of preference queries and our novel generator based model we give special attention to the aspects of the implementation and the practical experiences. We provide a sketch of the implementation and summarize our user studies which have been done in a joint project with an industrial partner.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2004.11a
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• pp.189-192
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• 2004

The present much information of internet has to exist for innumerable user so that couldn't satisfy there's a variety of demand. so they have a demerit that search unnecessary information. However Web service is different with other mass media because It is possible that enable Mass Customization for Personalization strategy. In The paper suggest reasoning system that detect user connection location by using indirect abstraction techniques a kind of Case-based reasoning techniques.

• Asia pacific journal of information systems
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• v.14 no.3
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• pp.121-144
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• 2004

Developing a personalized system on a user's behalf which is working around the Internet-based marketplace is one of the challenging issues in intelligent e-business, especially mobile commenrce. It has been highly recommended that such a mobile personalized system has to perceive the user's needs a priori by tracking user's current context such as location with activity and then to identify the current needs dynamically and proactively. Automatically and unobtrusively getting user's context is an inevitable feature for the development of autonomous mobile commenrce. However, personalization methodologies and their feasible architectures for context-aware mobile commerce have been so far very rare. Hence, this paper aims to propose a context-aware mobile commerce development methodology by applying agent and semantic web technologies for personalized reminder system, which is one of the mobile commerce support system. We revisited associationism to understand a buyer's need identification process and adopt the process as 'purchase based on association' to implement a personalized reminder system. Based on this approach, we have showed how the agent-based semantic web service system can be used to realize need-aware reminder system. NAMA(Need-Aware Multi-Agent), a prototype system, has been implemented to show the feasibility of the methodology and framework under mobile setting proposed in this paper. NAMA embeds bluetooth-based location tracking module and identify what a user is currently looking at through her/his mobile device such as PDA. Based on these capabilities, NAMA considers the context, user profile with preferences, and information about currently available services, to aware user's current needs and then link her/him to a set of services, which are implemented as web services.

• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.5B
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• pp.542-551
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• 2009

We proposed the context based user profile which is aware of its user's situation and based on user's situation it recommends personalized services. The user profile which consists of (context, service) pair can be acquired by the context and the service usage of a user; it then can be used to recommend personalized services for the user. In this paper, we show how they can be evolved without previously known user information so that not to violate privacy during the learning phase; in the result our user profile can be applied to any new environment without any modification to model only except context profiles. Using context-awareness based user profile, the service usage pattern of a user can be learned by the union of contexts and the preferred services can be recommended by the current environments. Finally, we evaluate the precision of proposed approach using simulation with data sets of UCI depository and Weka tool-kit.

• Journal of Digital Convergence
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• v.10 no.2
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• pp.293-297
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• 2012

In order to provide with personalized recommendation service in context-awareness environment, the collected context data should be analyzed fast and the objective of user should be able to inferred effectively. But, the context collected from the mobile devices is not suitable for applying the existing inference algorithms as they are due to the omission or uncertainty of information and the efficient algorithms are required for mobile environment. In this paper, the behavior pattern was classified using naive bayes classification for minimize the loss caused by the omission or error of information. And pattern matching was used to effectively learn of the users inclination and infer the behavior purpose. The accuracy of the suggested inference model was evaluated by applying to the application recommendation service in the smart phones.

• Journal of KIISE:Computer Systems and Theory
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• v.36 no.6
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• pp.488-501
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• 2009

In ubiquitous environments, invisible devices and software are connected to one another to provide convenient services to users. In order to provide such services, we must have mobile devices that connect users and services. But such services are usually limited to those served on a single mobile device. To resolve the resource limitation problem of mobile devices, a nearby resource sharing research has been studied. Also, not only the nearby resource share but also a resource recommendation through context-based resource reasoning has been studied such as an UMO Project. The UMO Project share and manage the various context information for the personalization resource recommendation and reason based on current context information. Also, should verify resource inference rules for reliable the resource recommendation. But, to create various context information requires huge cost and time in actuality. Thus, we propose a inference verification tool called USim to resolve problem. The proposed inference verification tool provides convenient graphic user interfaces and it easily creates context information. The USim exactly verifies new inference rules through dynamic changes of context information.

• Journal of the Korean Society for information Management
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• v.23 no.2
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• pp.147-165
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• 2006

The explosive growth of Web-based educational resources requires a new approach for accessing relevant information effectively. Social searching in the context of social navigation is one of several answers to this problem, in the domain of information retrieval. It provides users with not merely a traditional ranked list, but also with visual hints which can guide users to information provided by their colleagues. A personalized and context-dependent social searching system has been implemented on a platform called KnowledgeSea II, an open-corpus Web-based educational support system with multiple access methods. Validity tests were run on a variety of aspects and results have shown that this is an effective way to help users access relevant, essential information.

• Journal of Digital Convergence
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• v.15 no.9
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• pp.137-146
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• 2017

This study explores the impacts of application attributes of coffee chains on consumer's re-purchase decision-making processes in the context of coffee chains. We posited coffee quality, service quality, and physical environment as key service attributes of coffee chains and personalization, usefulness, economy, and convenience as key application attributes. The moderating effect of application attributes on the relationship between consumer satisfaction and repurchase intention was investigated. The theoretical framework was tested based on 382 consumers who frequently visit coffee chains and install their applications. PLS method was used to analysis the hypotheses. The theoretical model accounts for 48.1% of variance in customer satisfaction and 41.6% of variance in repurchase intention. The analysis results showed that personalization and convenience play an moderating effect on consumer's repurchase decision-making processes. Coffee quality and physical environment were found to have significant effects on customer satisfaction, while service quality does not significantly influence consumer satisfaction. Brand image has a significant effect on customer satisfaction and repurchase intention.