• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.2
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• pp.1-9
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• 2010

The increasing popularity of mobile devices, such as cellular phones, smart phones, and PDAs, has fostered the need to recommend more effective information in ubiquitous environments. We propose the recommendation method for mobile contents service using contexts and prefetching in ubiquitous environment. The proposed method enables to find some relevant information to specific user's contexts and computing system contexts. The prefetching has been applied to recommend to user more effectively. Our proposed method makes more effective information recommendation. The proposed method is conceptually comprised of three main tasks. The first task is to build a prefetching zone based on user's current contexts. The second task is to extract candidate information for each user's contexts. The final task is prefetch the information considering mobile device's resource. We describe a new recommendation.

• Journal of Digital Convergence
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• v.10 no.6
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• pp.275-281
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• 2012

This research proposes the method to develop the recommendation system of automobile parts using task ontology technology. The proposed intelligent recommendation system is designed to learn the assembly process of automobile parts and the automobile parts are composed by ontology method for the recommendation of the parts. Using hierarchical taxonomy based on is-a relationship, the relationship between each part that makes up automotive engine was set. Each part has each different weighted value according to the knowledge of automobile experts. The weighted value is created by the number of selection that the users of the automobile recommendation system select while using the system and the final value calculated by the multiplication of the weighted value, which is recorded within the system. As a result, the users can easily identify which factor in which part is important by the output in the order of the priority. The intelligent recommendation system for automobile parts is a system to inform of the assembly, the usage and the importance of automobile parts without any specialized knowledge by expressing the parts that are closely related with the applicable parts when selecting any part on the basis of the generated data for the automobile parts that are difficult to access by users.

• Journal of Convergence for Information Technology
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• v.9 no.2
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• pp.23-33
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• 2019

Research and development(R&D) activities consist of analytical survey and state-of-the-art report writing for technical information. As R & D activities become more concrete, it often happens that they refer to related technical documents that were created in previous steps or created in previous similar projects. This paper proposes a research-task based reuse recommendation framework(RTRF), which is a reuse recommendation system that enables researchers to efficiently reuse the existing artifacts. In addition to the existing keyword-based retrieval and reuse, the proposed framework also provides reusable information that researchers may need by recommending reusable artifacts based on task similarity; other developers who have a similar task to the researcher's work can recommend reusable documents. A case study was performed to show the researchers' efficiency in the process of writing the technology trend report by reusing existing documents. When reuse is performed using RTRF, it can be seen that documents of different stages or other research fields are reused more frequently than when RTRF is not used. The RTRF may contribute to the efficient reuse of the desired artifacts among huge amount of R&D documents stored in the repository.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.3
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• pp.58-64
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• 2005

Customer sensibility based recommendation agent system was developed to tailor to the customer the suggestion of goods and the description of store catalog in on-line shopping mall. The recommendation agent system composed of five modules and seven services including specialized algorithm. This study was to investigate the effectiveness of the customer sensibility based recommendation agent system in on-line shopping mall. This study asked 30 male and female students to perform the task in on-line shopping mall and facilitated them questionnaires. The questionnaires were administered to subjects to measure quality precision, ease of use, support of buying, purchasing power, future intention of the system. The study revealed that good part of the subjects positively evaluated the customer sensibility based recommendation system except for ease of use. The study on usability of the recommendation agent system has need to be performed in next. This paper shows that the satisfaction and the buying power of customers may be improved by presenting customer sensibility based recommendation in on-line shopping mall.

• Journal of Gifted/Talented Education
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• v.22 no.1
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• pp.173-196
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• 2012

The relationship of the teacher's recommendation and performance in gifted programs for the selected student by teacher's observations and nominations was analyzed in this study. The teacher's recommendation for 9 students selected by teacher's observations and nominations in institute of Science gifted Education of C National University of Education was analyzed for this purpose. The students were categorized into 4 groups depending on the description style and contents of the teacher's recommendation and 1 student was selected from each group for analysis. It was shown that the student, a1 who was described with cognitive characteristics of the gifted in episode style in the teacher's recommendation showed the aggressive task adherence and problem solving ability. The student, a2 who was described with emotional and social characteristics in episode style attended at the class in active attitude, but the student solved the problem by the assistance of the colleagues or the teacher. The student, b1 who was listed superficially in the teacher's recommendation showed the excellent problem solving ability based on the task adherence, experiment design ability and experiment manipulation ability. The student, b2 who was listed in superficially in the teacher's recommendation attended at the class in positive and upright attitude, but the task solving was lagged behind. It is concluded from the above results that the description on the cognitive area is necessary for the teacher's recommendation to have the usefulness in selecting gifted students.

• Journal of Gifted/Talented Education
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• v.24 no.4
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• pp.577-595
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• 2014

The purpose of this research was to investigate the possibility of peer recommendation and to provide a diverse perspectives identifying for gifted elementary school students. The subjects were 355 elementary school students who were either fourth, fifth, and sixth grade from D elementary school located in Gyeonggi province, 165 elementary school students who are either fourth, fifth, and sixth grade from J elementary school located in Gyeonggi province, and 16 teachers who were home-room teachers for students surveyed. This research investigate the difference of creative personality, achievement motivation, problem solving patterns, and task preference inventory between students recommended by peer and students not recommended by peer. The results of this research were as follows. Firstly, the students recommended by peer showed the significantly higher creative personality, achievement motivation, problem solving patterns, and task preference than the student not recommended by peer. Secondly, as a result of comparing the students recommended by peer and students not recommended by peer for each grade, fifth and sixth grade students showed a significantly higher score in the test than other graders. Fourth grade students showed significantly higher score in every sections in the test, except for the section of creative personality. Therefore, the peer recommandation method can be applied to fifth, and sixth graders. Thirdly, the student recommended by both teacher and peer showed significantly higher problem solving patterns, and task preference scores than the student recommended only by teacher. Therefore, peer recommendation method can be an useful data for complementing teacher recommendation and it can identify gifted elementary school students.

• International Journal of Advanced Culture Technology
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• v.8 no.4
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• pp.167-176
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• 2020

Recommendation Systems is the top requirements for many people and researchers for the need required by them with the proper suggestion with their personal indeed, sorting and suggesting doctor to the patient. Most of the rating prediction in recommendation systems are based on patient's feedback with their information regarding their treatment. Patient's preferences will be based on the historical behaviour of similar patients. The similarity between the patients is generally measured by the patient's feedback with the information about the doctor with the treatment methods with their success rate. This paper presents a new method of predicting Top Ranked Doctor's in recommendation systems. The proposed Recommendation system starts by identifying the similar doctor based on the patients' health requirements and cluster them using K-Means Efficient Clustering. Our proposed K-Means Clustering with Content Based Doctor Recommendation for Cancer (KMC-CBD) helps users to find an optimal solution. The core component of KMC-CBD Recommended system suggests patients with top recommended doctors similar to the other patients who already treated with that doctor and supports the choice of the doctor and the hospital for the patient requirements and their health condition. The recommendation System first computes K-Means Clustering is an unsupervised learning among Doctors according to their profile and list the Doctors according to their Medical profile. Then the Content based doctor recommendation System generates a Top rated list of doctors for the given patient profile by exploiting health data shared by the crowd internet community. Patients can find the most similar patients, so that they can analyze how they are treated for the similar diseases, and they can send and receive suggestions to solve their health issues. In order to the improve Recommendation system efficiency, the patient can express their health information by a natural-language sentence. The Recommendation system analyze and identifies the most relevant medical area for that specific case and uses this information for the recommendation task. Provided by users as well as the recommended system to suggest the right doctors for a specific health problem. Our proposed system is implemented in Python with necessary functions and dataset.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1708-1727
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• 2021

In the advent of the twenty-first century, human beings began to closely interact with technology. Today, technology is developing, and as a result, the world wide web (www) has a very important place on the Internet and the significant task is fulfilled by Web services. A lot of Web services are available on the Internet and, therefore, it is difficult to find matching Web services among the available Web services. The recommendation systems can help in fixing this problem. In this paper, our observation was based on the recommended method such as the collaborative filtering (CF) technique which faces some failure from the data sparsity and the cold-start problems. To overcome these problems, we first applied an ontology-based clustering and then the k-nearest neighbor (KNN) algorithm for each separate cluster group that effectively increased the data density using the past user interests. Then, user ratings were predicted based on the model-based approach, such as singular value decomposition (SVD) and the predictions used for the recommendation. The evaluation results showed that our proposed approach has a less prediction error rate with high accuracy after analyzing the existing recommendation methods.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.47-52
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• 2022

The current study investigates how student-teacher interaction can be developed through task-based teaching in undergraduate students' Saudi teaching and learning context. An experiment was conducted for five weeks on 85 male undergraduate students at a Saudi public university based in Jeddah, Saudi Arabia. The study investigated different types of student-teacher interaction through task-based teaching (speaking activities). The results revealed that the experimental group (43 students) evinced much more enthusiasm, willingness, engagement and readiness in their inclass participation than their peers in the control group (42 students). The student-teacher interaction also helped students to be more responsive to general and specific topics in speaking activities. The study recommends that decision-makers in education make student-teacher interaction part of the student's monthly assessment. It also recommends that more efforts be made to foster the awareness of students, teachers, and parents awareness of the academic and non-academic importance of interaction. One final recommendation of the research is that student-teacher interaction should be more emphasized and integrated into the school curriculum and adopted as a critical teaching strategy.

• Journal of Information Processing Systems
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• v.9 no.3
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• pp.435-460
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• 2013

Recommender systems are popular applications that help users to identify items that they could be interested in. A recent research area on recommender systems focuses on detecting several kinds of inconsistencies associated with the user preferences. However, the majority of previous works in this direction just process anomalies that are intentionally introduced by users. In contrast, this paper is centered on finding the way to remove non-malicious anomalies, specifically in collaborative filtering systems. A review of the state-of-the-art in this field shows that no previous work has been carried out for recommendation systems and general data mining scenarios, to exactly perform this preprocessing task. More specifically, in this paper we propose a method that is based on the extraction of knowledge from the dataset in the form of rating regularities (similar to frequent patterns), and their use in order to remove anomalous preferences provided by users. Experiments show that the application of the procedure as a preprocessing step improves the performance of a data-mining task associated with the recommendation and also effectively detects the anomalous preferences.