• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.5826-5841
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• 2019

In our previous research, we proposed a tag emotion-based item recommendation scheme. The ternary associations among users, items, and tags are described as a three-order tensor in order to capture the emotions in tags. The candidates for recommendation are created based on the latent semantics derived by a high-order singular value decomposition technique (HOSVD). However, the tensor is very sparse because the number of tagged items is smaller than the amount of all items. The previous research do not consider the previous behaviors of users and items. To mitigate the problems, in this paper, the item-based collaborative filtering scheme is used to build an extended data. We also apply the probabilistic ranking algorithm considering the user and item profiles to improve the recommendation performance. The proposed method is evaluated based on Movielens dataset, and the results show that our approach improves the performance compared to other methods.

• Journal of Information Technology Applications and Management
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• v.16 no.3
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• pp.17-28
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• 2009

This paper presents a novel method for evaluating the usefulness of online product recommendation. Previous studies on evaluating recommendation systems have mostly relied on two methods : testing the accuracy of estimating user preferences by recommendation systems, or empirically testing the effectiveness with lab experiments involving human participants. The former does not measure the usefulness directly and hence can be misleading; the latter is expensive in that it requires a working online store System and test participants. In order to address the problems, the proposed approach uses simulation to imitate customer behavior and evaluate the usefulness of recommendation. Models for user behavior and an abstract Internet store are developed for simulation. Actual simulation experiments are performed to illustrate the use of the approach.

• Journal of the Korea Safety Management & Science
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• v.10 no.4
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• pp.309-314
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• 2008

Many studies on predicting and recommending information and products have been studying to meet customers' preference. Unnecessary information should be removed to satisfy customers' needs in massive information. The some information filtering methods to remove unnecessary information have been suggested but these methods have scarcity and scalability problems. Therefore, this paper explores a personalized recommendation system based on artificial neural network (ANN) to solve these problems. The insurance product recommendation is adapted as an example to demonstrate the proposed method. The proposed recommendation system is expected to recommended a suitable and personalized insurance products for customers' satisfaction.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.3
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• pp.165-170
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• 2007

Recommendation systems provide users with proper services using context information being input from many sensors occasionally under ubiquitous computing environment. But in case there isn't sufficient context information for service recommendation in spite of much context information, there can be problems of resulting in inexact result. In addition, in the quantification step to use context information, there are problems of classifying context information inexactly because of using an absolute classification course. In this paper, we solved the problem of lack of necessary context information for service recommendation by using dynamic profile information. We also improved the problem of absolute classification by using a relative classification of context information in quantification step. As the result of experiments, expectation preference degree was improved by 7.5% as compared with collaborative filtering methods using an absolute quantification method where context information of P2P mobile agent is used.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.2903-2923
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• 2023

Knowledge recommendation is a type of recommendation system that recommends knowledge content to users in order to satisfy their needs. Although using graph neural networks to extract data features is an effective method for solving the recommendation problem, there is information loss when modeling real-world problems because an edge in a graph structure can only be associated with two nodes. Because one super-edge in the hypergraph structure can be connected with several nodes and the effectiveness of knowledge graph for knowledge expression, a dual-channel hypergraph convolutional neural network model (DCHC) based on hypergraph structure and knowledge graph is proposed. The model divides user data and knowledge data into user subhypergraph and knowledge subhypergraph, respectively, and extracts user data features by dual-channel hypergraph convolution and knowledge data features by combining with knowledge graph technology, and finally generates recommendation results based on the obtained user embedding and knowledge embedding. The performance of DCHC model is higher than the comparative model under AUC and F1 evaluation indicators, comparative experiments with the baseline also demonstrate the validity of DCHC model.

• 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.

• Journal of the Korea Society of Computer and Information
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• v.19 no.8
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• pp.73-81
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• 2014

This study proposed development method for practical use techniques compromise data storage layer, data processing layer, data analysis layer, visualization layer. Data of storage, process, analysis of each phase can see visualization. After data process through Hadoop, the result visualize from Mahout. According to this course, we can capture several features of customer, we can choose recommendation of financial item on time. This study introduce background and problem of big data and discuss development method and case study that how to create big data has new business opportunity through financial item recommendation case.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.12
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• pp.461-468
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• 2018

Developers spend a significant amount of time exploring and trying to understand source code to find a source location to modify. To reduce such time, existing studies have recommended the source location using statistical language model techniques. However, in these techniques, the recommendation does not occur if input data does not exactly match with learned data. In this paper, we propose a code location recommendation method using Recurrent Neural Networks and interaction histories, which does not have the above problem of the existing techniques. Our method achieved an average precision of 91% and an average recall of 71%, thereby reducing time for searching and exploring code more than the existing recommendation techniques.

• Journal of Internet Computing and Services
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• v.20 no.4
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• pp.73-79
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• 2019

Selecting a proper journal to submit a research paper is a difficult task for researchers since there are many journals and various decision factors to consider during the decision process. For this reason, journal recommendation services are exist as a kind of intelligent research assistant which recommend potential journals. The existing services are executing a recommendation based on topic similarity and numerical filtering. However, it is impossible to calculate topic similarity when a researcher does not input paper data, and difficult to input clear numerical values for researchers. Therefore, the journal recommendation method which consider the importance of decision factors is proposed by constructing the preference matrix based on the paper publication history of a researcher. The proposed method was evaluated by using the actual publication history of researchers. The experiment results showed that the proposed method outperformed the compared methods.

• Journal of Korea Society of Digital Industry and Information Management
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• v.7 no.2
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• pp.19-27
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• 2011

Recent development of information technologies produces a lot of community services. Social Network Service is one of the community services on the world wide webs. In the Social Network Service, a user can register other users as friends and enjoy communication through a virtual message. Previous researches show a few social service methods using manually generated tagging. However, the manual social tagging is not widely used in many social network services. Moreover, they do not consider ubiquitous computing environment. We propose a recommendation method for social service using contexts in ubiquitous environment. Our method scores documents based on context tags and social network services. Our social scoring model is computed by both a tagging score of a document and a tagging score of a document that was tagged by a user's friends.