• Journal of the Korean Society of Food Culture
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• v.32 no.5
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• pp.421-436
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• 2017

This study was conducted to examine the effects of Chinese perceptions of quality attributes on customer's satisfaction, revisit intention and recommendation intention for coffee shops in Beijing, China. Subjects of this study included 200 customers who had visited a coffee shop at least once during the last year. Statistical analyses were performed using SPSS v23.0 and AMOS v21.0. In this study, the majority of customers visited a coffee shop once or twice a week with friends. Respondents preferred tall-sized warm coffee in the store. The coffee shop quality attributes of were derived from five exploratory factors identified upon analysis of 30 observational variables. It was important to maintain and strengthen the quality attributes of coffee shops in this area because IPA(Importance Performance Analysis) analysis showed that "Doing great, keep it well" part was a desirable area because it had high importance and performance. Finally, path analysis revealed that customer satisfaction was influenced by employee attitude and affected revisit intention and recommendation intention.

• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.1114-1121
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• 2021

In this paper, we implement a recipe recommendation system using ingredient harmonization analysis based on recipe data. The proposed system receives an image of a food ingredient purchase receipt to recommend ingredients and recipes to the user. Moreover, it performs preprocessing of the receipt images and text extraction using the OCR algorithm. The proposed system can recommend recipes based on the combined data of ingredients. It collects recipe data to calculate the combination for each food ingredient and extracts the food ingredients of the collected recipe as training data. And then, it acquires vector data by learning with a natural language processing algorithm. Moreover, it can recommend recipes based on ingredients with high similarity. Also, the proposed system can recommend recipes using replaceable ingredients to improve the accuracy of the result through preprocessing and postprocessing. For our evaluation, we created a random input dataset to evaluate the proposed recipe recommendation system's performance and calculated the accuracy for each algorithm. As a result of performance evaluation, the accuracy of the Word2Vec algorithm was the highest.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.49-50
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• 2006

Digital satellite systems are usually integrated with terrestrial systems to provide various services, and in these cases they should satisfy the performance objectives defined by the terrestrial systems. Recommendation ITU-R S.1062 specifies the performance of digital satellite systems. The performance objectives were given in terms of bit error probability divided by the average number of errors per burst versus percentage of time. This paper presents theoretical method to estimate performance measure of digital satellite systems defined in Recommendation ITU-R S.1062. We show performance estimation results of duo-binary Turbo codes, and verify them by comparing to the simulation results.

• Journal of Information Management
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• v.33 no.1
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• pp.1-17
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• 2002

This study examined several recommendation techniques to construct an effective book recommender system in a library. Experiments revealed that a hybrid recommendation technique is more effective than either collaborative filtering or content-based filtering technique in recommending books to be borrowed in an academic library setting. The recommendation technique based on association rule turned out the lowest in performance.

• Journal of Internet Computing and Services
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• v.21 no.1
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• pp.111-117
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• 2020

Dessert Ateliers (DA) are small shops that sell high-end homemade desserts such as macaroons, cakes, and cookies, and their popularity is increasing according to the emergence of small luxury trends. Even though each DA sells the same kinds of desserts, they are differentiated by the personality of their pastry chef; thus, there is a need to purchase desserts online that customers cannot see and purchase offline, and thus dessert e-commerce has emerged. However, it is impossible for customers to identify all the information of each DA and clearly understand customers' preferences when buying desserts through the dessert e-commerce. When a dessert e-commerce service provides a DA recommendation service, customers can reduce the time they hesitate before making a decision. Therefore, this paper proposes two kinds of DA recommendation method: a clustering-based recommendation method that calculates the similarity between customers' content and DAs and a dynamic weighting-based recommendation method that trains the importance of decision factors considering customer preferences. Various experiments were conducted using a real-world dataset to evaluate the performance of the proposed methods and it showed satisfactory results.

• 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.

• Journal of Information Technology Applications and Management
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• v.29 no.3
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• pp.43-55
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• 2022

In this study, with the goal of developing a deep learning-based product recommendation model for effective matching of influencers and products, a deep learning model with a collaborative filtering model combined with generalized matrix decomposition(GMF), a collaborative filtering model based on multi-layer perceptron (MLP), and neural collaborative filtering and generalized matrix Factorization (NeuMF), a hybrid model combining GMP and MLP was developed and tested. In particular, we utilize one-class problem free boosting (OCF-B) method to solve the one-class problem that occurs when training is performed only on positive cases using implicit feedback in the deep learning-based collaborative filtering recommendation model. In relation to model selection based on overall experimental results, the MLP model showed highest performance with weighted average precision, weighted average recall, and f1 score were 0.85 in the model (n=3,000, term=15). This study is meaningful in practice as it attempted to commercialize a deep learning-based recommendation system where influencer's promotion data is being accumulated, pactical personalized recommendation service is not yet commercially applied yet.

• 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 Intelligence and Information Systems
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• v.25 no.4
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• pp.89-103
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• 2019

Recently, various approaches to recommendation systems have been studied in terms of the quality of recommendation system. A recommender system basically aims to provide personalized recommendations to users for specific items. Most of these systems always recommend the most relevant items of users or items. Traditionally, the evaluation of recommender system quality has focused on the various predictive accuracy metrics of these. However, recommender system must be not only accurate but also useful to users. User satisfaction with recommender systems as an evaluation criterion of recommender system is related not only to how accurately the system recommends but also to how much it supports the user's decision making. In particular, highly serendipitous recommendation would help a user to find a surprising and interesting item. Serendipity in this study is defined as a measure of the extent to which the recommended items are both attractive and surprising to the users. Therefore, this paper proposes an application of serendipity measure to recommender systems to evaluate the performance of recommender systems in terms of recommendation system quality. In this study we define relevant or attractive unexpectedness as serendipity measure for assessing recommendation systems. That is, serendipity measure is evaluated as the measure indicating how the recommender system can find unexpected and useful items for users. Our experimental results show that highly serendipitous recommendation such as item-based collaborative filtering method has better performance than the other recommendations, i.e. user-based collaborative filtering method in terms of recommendation system quality.

• 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.