• 한국컴퓨터정보학회논문지
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• 제15권11호
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• pp.205-213
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• 2010

연관규칙 마이닝 기법 중에 하나인 FP-트리 알고리즘을 이용하는 추천시스템이 시도되고 있다. 본 논문에서는 트랜�Ъ� 데이터베이스로부터 빈발 2-항목집합만을 추출하여 연관규칙을 생성하는 변형된 FP-알고리즘을 사용하는 추천시스템을 제안하였다. 제안된 추천시스템은 전처리 모듈, 학습 모듈, 추천 모듈 및 평가 모듈로 구성되었다. 제안된 추천시스템의 실험을 통하여 상품 추천의정확률과 재현율과 F-Measure와 성공률과 추천실행시간을 수행하였으며, 순차패턴 마이닝 기법을 사용하는 추천시스템과의 성능을 비교분석 하였다. 순차패턴 마이닝기법을 사용하는 추천시스템과 학습 성능, 추천 성능을 비교한 결과 학습 성능은 5배 이상 향상되었으며, 추천 성능은 20%이상 향상 되었다. 결론적으로, 순차패턴 추천시스템과 같은 데이터를 가지고 실험하여 추천시스템 성능의 타당성에는 보다 나은 시스템임을 입증 하였다.

• Journal of the Korean Data and Information Science Society
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• 제17권1호
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• pp.77-86
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• 2006

This study is to investigate the error of prediction value with related variables from the recommended system and to examine the error of prediction value with related variables. To decrease the error on the collaborative recommended system on user based, this research explored the effects on the prediction related response pair between raters' demographic variables and Pearson's coefficient and sparsity. The result shows comparative analysis between existing error of prediction value and conditioned one.

• International Journal of Advanced Culture Technology
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• 제11권4호
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• pp.385-392
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• 2023

The smart tourism service provides tourists with personal travel planner services and context-awareness-based tour guide services. In this paper, we propose the personal travel planner service that creates my travel itinerary using the smart tourism app and the travel planner system. The smart tourism app provides recommended travel products and POI tourist information used to create my travel itinerary. The smart tourism app also provides the smart tourism chatbot service that allows users to select POI tourist information easily and conveniently. The travel planner system consists of the smart tourism information system and the smart tourism chatbot system. The smart tourism information system provides users with travel planner services, recommended travel products, and POI tourism information through the smart tourism app. The smart tourism chatbot system consists of named entity recognition (NER), dialogue state tracking (DST), and Neo4J servers, and provides chatbot services as a smart tourism app. Users can create their own travel itinerary, modify the travel itinerary while traveling, and then register it as a recommended travel product to users, including acquaintances.

• 한국컴퓨터정보학회논문지
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• 제20권11호
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• pp.89-95
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• 2015

In this paper, we propose the method and development of custom-made golf insole recommender system to optimize the foot balance during golf swing. This system development procedures are as follows : (1) Using the measured data of the golf swing, the analysis of the individual golf hitting and balance will be done. (2) Based on the analysis results, the system will recommend the golf custom-made insole to optimize the individual balance using recommender algorithm. (3) After the golf custom-made insole is recommended, the modeling and design of the recommended insole is processed. Golf custom-made insole will be possible to reduce the excessive shaking and increase the lower-body supporting force. Therefore, we have expected that the recommended insole will improve the swing results through the optimization of golf swing balance. In the future, it is necessary to secure the higher validity and reliability through the more diverse experiments and research.

• 자동차안전학회지
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• 제8권4호
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• pp.18-23
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• 2016

The objective of this study is to present the inspection standards of motorcycle brake system affecting motorcycle traffic accident. The inspection standards of motorcycle brake system recommended by CITA (International Motor Vehicle Inspection Committee) are studied. The brake performance for preventing the traffic accident is assessed by the brake efficiency. Considering the KMVSS (Korean Motor Vehicle Safety Standards) and the inspection standards of CITA, United Kingdom and Japan, the brake performance in the inspection standards of motorcycle is suggested. It is recommended that the efficiency of independent front and rear brakes are 40% and 27%, respectively. It is recommended that the efficiency of combined front and rear brakes is 50%.

• Journal of Community Nutrition
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• 제7권2호
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• pp.107-113
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• 2005

This study was conducted to develop a web expert system for nutrition counseling and menu management. This program manipulates a food, dish and menu and search database that has been developed. Clients can select a recommended general and therapeutic menu using this system. The web expert system can analyze nutrients in menus and compare nutrient contents of menus with Korean Recommended Dietary Allowances. It can access the food, dish and menu database. The expert menu database can insert, store and generate the synthetic information of age, sex, and therapeutic purpose of disease. With investigation and analysis of the client's needs, the menu planning program on the internet has been continuously developed. This system consists of the database that reaches to the food composition, the dishes and the menu. Clients can search food composition and conditional food based on nutrient name and amounts. This system is able to draw up the food with its order in dish. The menu planning can be organized and nutrients analysis can be compared with Korea Recommended Allowance. This system is able to read the nutrient composition of the each food, the dish and the menu. The results of analysis is presented quickly and accurately. Therefore it can be used by not only usual people but also dietitians and nutritionists who take charge of making a menu and experts in the field of food and nutrition. It is expected that the web expert system can be useful of nutrition education, nutrition counseling and expert menu management.

• International Journal of Advanced Culture Technology
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• 제8권4호
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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.

• 한국멀티미디어학회논문지
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• 제23권1호
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• pp.31-42
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• 2020

During the 4th Industrial Revolution, service platforms utilizing diverse contents are emerging, and research on recommended systems that can be customized to users to provide quality service is being conducted. hybrid recommendation systems that provide high accuracy recommendations are being researched in various domains, and various filtering techniques, machine learning, and deep learning are being applied to recommended systems. However, in a recommended service environment where data must be analyzed and processed real time, the accuracy of the recommendation is important, but the computational speed is also very important. Due to high level of model complexity, a hybrid recommendation system or a Deep Learning-based recommendation system takes a long time to calculate. In this paper, a Cascade-hybrid recommended algorithm is proposed that can reduce the computational time while maintaining the accuracy of the recommendation. The proposed algorithm was designed to reduce the complexity of the model and minimize the computational speed while processing sequentially, rather than using existing weights or using a hybrid recommendation technique handled in parallel. Therefore, through the algorithms in this paper, contents can be analyzed and recommended effectively and real time through services such as SNS environments or shared economy platforms.

• Journal of Information Technology Applications and Management
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• 제21권4호
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• pp.203-223
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• 2014

This study proposes a novel context-aware recommender system, which is designed to recommend the items according to the customer's responses to the previously recommended item. In specific, our proposed system predicts the user's emotional state from his or her responses (such as facial expressions and movements) to the previous recommended item, and then it recommends the items that are similar to the previous one when his or her emotional state is estimated as positive. If the customer's emotional state on the previously recommended item is regarded as negative, the system recommends the items that have characteristics opposite to the previous item. Our proposed system consists of two sub modules-(1) emotion prediction module, and (2) responsive recommendation module. Emotion prediction module contains the emotion prediction model that predicts a customer's arousal level-a physiological and psychological state of being awake or reactive to stimuli-using the customer's reaction data including facial expressions and body movements, which can be measured using Microsoft's Kinect Sensor. Responsive recommendation module generates a recommendation list by using the results from the first module-emotion prediction module. If a customer shows a high level of arousal on the previously recommended item, the module recommends the items that are most similar to the previous item. Otherwise, it recommends the items that are most dissimilar to the previous one. In order to validate the performance and usefulness of the proposed recommender system, we conducted empirical validation. In total, 30 undergraduate students participated in the experiment. We used 100 trailers of Korean movies that had been released from 2009 to 2012 as the items for recommendation. For the experiment, we manually constructed Korean movie trailer DB which contains the fields such as release date, genre, director, writer, and actors. In order to check if the recommendation using customers' responses outperforms the recommendation using their demographic information, we compared them. The performance of the recommendation was measured using two metrics-satisfaction and arousal levels. Experimental results showed that the recommendation using customers' responses (i.e. our proposed system) outperformed the recommendation using their demographic information with statistical significance.

• Asia pacific journal of information systems
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• 제29권4호
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• pp.856-874
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• 2019

The performance of recommender systems (RS) has been measured mainly in terms of accuracy. However, there are other aspects of performance that are difficult to understand in terms of accuracy, such as coverage, serendipity, and satisfaction with recommended results. Moreover, particularly with RSs that suggest multiple items at a time, such as YouTube, user satisfaction with recommended results may vary not only depending on their accuracy, but also on their configuration, content, and design displayed to the user. This is true when classifying an RS as a single RS with one recommended result and as a multiple RS with diverse results. No empirical analysis has been conducted on the influence of the content and distribution of recommendation items on user satisfaction. In this study, we propose a research model representing the content and distribution of recommended items and how they affect user satisfaction with the RS. We focus on RSs that recommend multiple items. We performed an empirical analysis involving 149 YouTube users. The results suggest that user satisfaction with recommended results is significantly affected according to the HHI (Herfindahl-Hirschman Index). In addition, satisfaction significantly increased when the recommended item on the top of the list was the same category in terms of content that users were currently watching. Particularly when the purpose of using RS is hedonic, not utilitarian, the results showed greater satisfaction when the number of views of the recommended items was evenly distributed. However, other characteristics of selected content, such as view count and playback time, had relatively less impact on satisfaction with recommended items. To the best of our knowledge, this study is the first to show that the category concentration of items impacts user satisfaction on websites recommending diverse items in different categories using a content-based filtering system, such as YouTube. In addition, our use of the HHI index, which has been extensively used in economics research, to show the distributional characteristics of recommended items, is also unique. The HHI for categories of recommended items was useful in explaining user satisfaction.