• 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 Digital Convergence
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• v.11 no.8
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• pp.213-218
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• 2013

In this paper, when vehicle inspection is made, the check way is recommended based on the intelligent and personalized in the workplace, education, and other space-time according to the current situation. These results increase productivity, reduce costs, and improve performance. So we designed vehicle inspection recommendation system using ontology. Recommendation method is that components connected concept are shown according to weight value. if components are connected with other concept, the components are extended.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2006.06a
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• pp.344-353
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• 2006

Nowadays many people participate in online communities for information sharing. But most recommender systems are designed for personalization of individual user, so it is necessary to develop a recommendation procedure for group users, such as participants in online communities. This paper proposes a group recommender system to recommend books for group users in online communities. For such a purpose, we suggest a group recommendation procedure consisting of two phases. The first phase is to generate recommendation list for 'big user' using collaborative filtering, and the second phase is to remove irrelevant books among previous list reflecting the preference of each individual user. The procedure is explained step by step with an illustrative example. And this procedure can potentially be applied to other domains, such as music, movies and etc.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.5
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• pp.609-617
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• 2011

This paper proposes a personalized food recommendation system using user profiles auto-generated from Twitter. The user profiles are generated by extracting nouns from Twitter, and calculating emotional scores according to whether each noun is collocated with emotion words. Representative noun information for each food is constructed by analyzing web pages relevant to foods. Appropriate foods for users can be recommended by calculating similarities among the extracted resources. The proposed system has an advantage in that it can always recommend foods even if a user is a newcomer.

• Journal of Intelligence and Information Systems
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• v.12 no.3
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• pp.111-126
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• 2006

The context-awareness is one of the core technologies in ubiquitous computing environment. In this research, we incorporated the capability of context-awareness in a case-based music recommendation system. Our proposed system consists of Intention Module and Recommendation Module. The Intention Module infers whether a user wants to listen to the music or not from the environmental context information. Then, the Recommendation Module selects songs from the songs that are listened by similar users in similar context, and recommends them to the user. The results showed that our proposed system outperformed the traditional case-based music recommendation system in accuracy by about 9% point.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2399-2413
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• 2021

A recommendation system is an information filter tool, which uses the ratings and reviews of users to generate a personalized recommendation service for users. However, the cold-start problem of users and items is still a major research hotspot on service recommendations. To address this challenge, this paper proposes a high-efficient hybrid recommendation system based on Fuzzy C-Means (FCM) clustering and supervised learning models. The proposed recommendation method includes two aspects: on the one hand, FCM clustering technique has been applied to the item-based collaborative filtering framework to solve the cold start problem; on the other hand, the content information is integrated into the collaborative filtering. The algorithm constructs the user and item membership degree feature vector, and adopts the data representation form of the scoring matrix to the supervised learning algorithm, as well as by combining the subjective membership degree feature vector and the objective membership degree feature vector in a linear combination, the prediction accuracy is significantly improved on the public datasets with different sparsity. The efficiency of the proposed system is illustrated by conducting several experiments on MovieLens dataset.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.177-184
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• 2021

All over the world, people are affected by many chronic diseases and medical practitioners are working hard to find out the symptoms and remedies for the diseases. Many researchers focus on the feature detection of the disease and trying to get a better health recommendation system. It is necessary to detect the features automatically to provide the most relevant solution for the disease. This research gives the framework of Health Recommendation System (HRS) for identification of relevant and non-redundant features in the dataset for prediction and recommendation of diseases. This system consists of three phases such as Pre-processing, Feature Selection and Performance evaluation. It supports for handling of missing and noisy data using the proposed Imputation of missing data and noise detection based Pre-processing algorithm (IMDNDP). The selection of features from the pre-processed dataset is performed by proposed ensemble-based feature selection using an expert's knowledge (EFS-EK). It is very difficult to detect and monitor the diseases manually and also needs the expertise in the field so that process becomes time consuming. Finally, the prediction and recommendation can be done using Support Vector Machine (SVM) and rule-based approaches.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.1
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• pp.25-40
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• 2023

Recommendation systems are frequently used by users to generate intelligent automatic decisions. In the study of movie recommendation system, the existing approach uses largely collaboration and content-based filtering techniques. Collaborative filtering considers user similarity, while content-based filtering focuses on the activity of a single user. Also, mixed filtering approaches that combine collaborative filtering and content-based filtering are being used to compensate for each other's limitations. Recently, several AI-based similarity techniques have been used to find similarities between users to provide better recommendation services. This paper aims to provide the prospective expansion by deriving possible solutions through the analysis of various existing movie recommendation systems and their challenges.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.109-112
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• 2007

온라인 음악 시장이 점차 커지고 있다. 이에 따라 사용자를 위한 다양한 서비스가 요구되고 있다. 하지만 현재 적용되는 서비스는 통계적인 수치에 기반하는 순위권 나열 혹은 테마나 장르별 음악 소개에 그치고 있다. 따라서 본 논문에서는 사용자의 성향에 가까운 음악을 분석하고 이를 추천하는 방법을 제시한다. 음악 추천 시스템을 위해 우선 사용자의 성향을 분석하기 위하여 사용자가 청취했던 음악의 음파를 분석하여 특성을 추출하여 벡터로 나타낸다. 하지만 추출된 성향과 다른 음악의 성향을 비교해야 하는데 음악의 양이 방대하기 때문에 시간이 오래 걸릴 수 있다. 따라서 이 문제를 해결하기 위해 공간 분할을 통해 검색의 범위를 축소시키고, 음악을 빠르게 추천한다. 실험 결과, 사람의 주관적인 해석이 아닌 음파의 해석을 통해 보다 객관적이고 자동화된 추천 방법을 구현할 수 있었다. 그리고 같은 성질의 음악이 추천되어짐을 확인할 수 있었다.

• Korean Management Science Review
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• v.21 no.3
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• pp.39-54
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• 2004

This paper presents a multi-agent system for intelligent recommendation of travel plans to users. The goal of the system is to provide alternative and preferable travel plans to users when the availability of tickets is low such as in vacations, holidays, weekends, or peak seasons. The multiple agents in the system search for available alternatives for a target travel in collaboration with other agents and recommend best alternatives by analyzing them using a multi-criteria decision-making model. A prototype online travel support system was constructed and a simulation experiment was performed for evaluation and comparison with different travel planning strategies.