• Proceedings of the Korean Information Science Society Conference
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• 2002.04b
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• pp.244-246
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• 2002

The rapid increase of E-commerce market requires a solution to assist the buyer to find his or her interested items. The intelligent agent model is one of the approaches to help the buyers in purchasing items in outline market. In this paper, the user query processing model in the item recommendation agent is proposed. In the proposed model, the retrieval result is affected by the automatically generated queries from user preference information as well as the queries explicitly given by user. Therefore, the proposed model can provide the customized search results to each user.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.606-607
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• 2014

Due to Web Services' platform and language independent nature, many business corporations have used them for the integration of various applications. However, the growing amount of available Web Services on Web forms a new problem - how to select and recommend an appropriate Web Service that matches the user requirements. In this paper, we investigate recommendation methods in Web Services, and discuss their strength and limitations.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.1158-1159
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• 2013

It gives to users a difficulty for searching between this huge numbers of programs. Searching the best applications for our needs is a big challenge today. In this paper, we propose a study on collaborative filtering based app recommendation system. The proposed method is composed of three steps. In the first step, we extract the data set from the target website. In the second step, we parse the extracted raw data according to the types, and store in a database. In the third, we perform recommendations based on the stored data in database.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.369-373
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• 2022

In the decision-making domain, a document warehouse is designed to meet the analysis needs of users who may have a wide variety of analysis purposes. In this paper, we propose to integrate the preferences and interactions of users based on profiles to the concept of document warehouses. These profiles guarantee the integration of personalized documents and the collaborative recommendation of documents between different users sharing common interests.

• Journal of Digital Convergence
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• v.12 no.8
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• pp.329-335
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• 2014

Collaborative filtering has been popular in commercial recommender systems, as it successfully implements social behavior of customers by suggesting items that might fit to the interests of a user. So far, most common method to find proper items for recommendation is by searching for similar users and consulting their ratings. This paper suggests a new similarity measure for movie recommendation that is based on genre interest, instead of differences between ratings made by two users as in previous similarity measures. From extensive experiments, the proposed measure is proved to perform significantly better than classic similarity measures in terms of both prediction and recommendation qualities.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.95-101
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• 2012

As the influence of social networking services across the societies becomes greatly higher, many of the domestic agencies are trying to communicate with users through the introduction of social networking services. In this paper, we present a reliable exhibition-related contents recommendation service to combine social networking service concept with the customized contents recommendation method we previously proposed. The proposed service may effectively and reliably recommend its users exhibition-related contents by exploiting their relationships in the social networks compared with the existing ones.

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.1075-1087
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• 2020

Aviation ICT technology is a convergence technology between aviation and electronics, and has a wide variety of applications, including navigation and education. Among them, in the field of aerial pilot training, there are many problems such as the possibility of accidents during training and the lack of coping skills for various situations. This raises the need for a simulated pilot training system similar to actual training. In this paper, pilot training data were collected in pilot training system using VR/AR to increase immersion in flight training, and Customized Pilot Training Platform with Collaborative Deep Learning in VR/AR Environment that can recommend effective training courses to pilots is proposed. To verify the accuracy of the recommendation, the performance of the proposed collaborative deep learning algorithm with the existing recommendation algorithm was evaluated, and the flight test score was measured based on the pilot's training data base, and the deviations of each result were compared. The proposed service platform can expect more reliable recommendation results than previous studies, and the user survey for verification showed high satisfaction.

• 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.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.6-10
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• 2009

In these days, recommendation service in mobile environments is in the limelight due to the spread of mobile devices and an increase of information owing to advancement of computer network. The restaurant recommendation system reflecting user preference was proposed. This system uses Bayesian network to model user preference and analytical hierarchical process to recommend restaurants, but static inference model for user preference used in the system has some limitations that cannot manage changing user preference and enormous user survey must be preceded. This paper proposes a learning method for Bayesian network based on user requests. The proposed method is implemented on mobile devices and desktop, and we show the possibility of the proposed method through experiments.

• The Journal of the Korea Contents Association
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• v.8 no.7
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• pp.17-25
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• 2008

The smart home environment focuses on recognizing the context and physical entities. And this is mainly focused on the personalized service supplied conversational interactions. In this paper, we proposed the recommendation using the context awareness based information filtering that dynamically applied by the context awareness as well as the meta data in the smart home. The proposed method defined the context information and recommended the profited service for the user’s taste using the context awareness based information filtering. Accordingly, the satisfaction of users and the quality of services will be improved the efficient recommendation by supporting the distributed processing as well as the mobility of services. Finally, to evaluate the performance of the proposed method, this study applies to MovieLens dataset in the OSGi framework, and it is compared with the performance of previous studies.