• Journal of Information Technology Services
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• v.6 no.2
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• pp.113-123
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• 2007

In ubiquitous computing environment such as ubiquitous marketplace (u-market), there is a need of providing context-based personalization service while considering the nomadic user preference and corresponding requirements. To do so, the recommendation systems should deal with the tremendous amount of context data. Hence, the purpose of this paper is to propose a novel recommendation method which provides the products-group list of the customers in u-market based on the shopping intention and preferences. We have developed FREPIRS(FREquent Purchased Item-sets Recommendation Service), which makes recommendation listof product-group, not individual product. Collaborative filtering and apriori algorithm are adopted in FREPIRS to build product-group.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06c
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• pp.88-90
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• 2006

최근 정보 통신기술의 발전으로 인하여 전자상거래가 확산되고 있는 실정이며, 이용하는 고객 또한 상당히 증가하고 있다. 고객의 활발한 구매 거래 활동으로 하루에도 아주 많은 양의 데이터가 생성되고 있는 실정이다. 이에 전자상거래의 웹 사이트 관리자나 경영자는 고객의 구매형태나 패턴의 특징을 파악하여 보다 효율적인 서비스를 고객에게 제공하기 위하여 현재까지 유사그룹의 고객 세분화를 적용하는 연구가 이루어지고 있다. 본 논문에서는 전자상거래에서 고객들의 정보를 분석하여 개인화하기 위한 방법으로 사용되는 고객 프로파일을 이용하여 고객세분화 하는데 적용을 하고자 한다. 기존 고객세분화의 통계적인 분석이 아닌 XML 기반의 고객 정보를 XPath를 이용하여 고객세분화에 필요한 규칙을 생성하고, 그 규칙을 바탕으로 고객 프로파일을 생성하는 방법과 프로파일을 이용한 군집에 따른 분석 결과 및 추천서비스를 소개하고자 한다.

• Proceedings of the Korean Society for Information Management Conference
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• 2016.08a
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• pp.49-55
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• 2016

본 연구에서는 물리, 생명분야 연구자들의 인구사회학적 특성이 기관리포지터리 수용의도에 미치는 영향을 살펴보았다. 그리고 이를 정보공유와 확산의 장으로 기관리포지터리를 활용할 수 있는 전략 수립의 근거자료로 활용하고자 하였다. 관련 문헌자료 조사, 설문조사와 심층면담을 수행한 연구결과는 다음과 같다. 첫째, 기관리포지터리 인지경로는 도서관과 모체기관의 홍보, 동료들의 추천순으로 나타났다. 둘째, 연구자의 학술정보자원 공유는 면대면이 가장 높게 나타났으며, 그다음으로 오픈액세스 기반 기관리포지터리, 연구그룹단위 웹사이트 순이었다. 셋째, 학술정보자원 유형별 공개는 심사후 학술논문이 가장 높게 나타났으며, 실험데이터에 대한 공개의사는 매우 저조한 것으로 나타났다. 넷째, 학술정보자원의 공개를 통해 연구자들은 연구성과에 대한 인정, 우선권확보, 연구역량 강화에 대한 기대감, 취업에 대한 기대감, 중복연구 가능성 축소를 기대하고 있었다. 향후 다양한 학문분야별 연구자의 특성과 행태분석을 통해 주제분야별 특성이 반영된 기관리포지터리 개발이 필요할 것으로 보인다.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.1-20
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• 2013

Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.

• KIISE Transactions on Computing Practices
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• v.21 no.11
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• pp.681-688
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• 2015

The k nearest neighbor (k-NN) graph construction is an important operation with many web-related applications, including collaborative filtering, similarity search, and many others in data mining and machine learning. Despite its many elegant properties, the brute force k-NN graph construction method has a computational complexity of $O(n^2)$, which is prohibitive for large scale data sets. Thus, (Key, Value)-based distributed framework, MapReduce, is gaining increasingly widespread use in Locality Sensitive Hashing which is efficient for high-dimension and sparse data. Based on the two-stage strategy, we engage the locality sensitive hashing technique to divide users into small subsets, and then calculate similarity between pairs in the small subsets using a brute force method on MapReduce. Specifically, generating a candidate group stage is important since brute-force calculation is performed in the following step. However, existing methods do not prevent large candidate groups. In this paper, we proposed an efficient algorithm for approximate k-NN graph construction by regrouping candidate groups. Experimental results show that our approach is more effective than existing methods in terms of graph accuracy and scan rate.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.25-44
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• 2013

Accessing movie contents has become easier and increased with the advent of smart TV, IPTV and web services that are able to be used to search and watch movies. In this situation, there are increasing search for preference movie contents of users. However, since the amount of provided movie contents is too large, the user needs more effort and time for searching the movie contents. Hence, there are a lot of researches for recommendations of personalized item through analysis and clustering of the user preferences and user profiles. In this study, we propose recommendation system which uses ontology based knowledge base. Our ontology can represent not only relations between metadata of movies but also relations between metadata and profile of user. The relation of each metadata can show similarity between movies. In order to build, the knowledge base our ontology model is considered two aspects which are the movie metadata model and the user model. On the part of build the movie metadata model based on ontology, we decide main metadata that are genre, actor/actress, keywords and synopsis. Those affect that users choose the interested movie. And there are demographic information of user and relation between user and movie metadata in user model. In our model, movie ontology model consists of seven concepts (Movie, Genre, Keywords, Synopsis Keywords, Character, and Person), eight attributes (title, rating, limit, description, character name, character description, person job, person name) and ten relations between concepts. For our knowledge base, we input individual data of 14,374 movies for each concept in contents ontology model. This movie metadata knowledge base is used to search the movie that is related to interesting metadata of user. And it can search the similar movie through relations between concepts. We also propose the architecture for movie recommendation. The proposed architecture consists of four components. The first component search candidate movies based the demographic information of the user. In this component, we decide the group of users according to demographic information to recommend the movie for each group and define the rule to decide the group of users. We generate the query that be used to search the candidate movie for recommendation in this component. The second component search candidate movies based user preference. When users choose the movie, users consider metadata such as genre, actor/actress, synopsis, keywords. Users input their preference and then in this component, system search the movie based on users preferences. The proposed system can search the similar movie through relation between concepts, unlike existing movie recommendation systems. Each metadata of recommended candidate movies have weight that will be used for deciding recommendation order. The third component the merges results of first component and second component. In this step, we calculate the weight of movies using the weight value of metadata for each movie. Then we sort movies order by the weight value. The fourth component analyzes result of third component, and then it decides level of the contribution of metadata. And we apply contribution weight to metadata. Finally, we use the result of this step as recommendation for users. We test the usability of the proposed scheme by using web application. We implement that web application for experimental process by using JSP, Java Script and prot$\acute{e}$g$\acute{e}$ API. In our experiment, we collect results of 20 men and woman, ranging in age from 20 to 29. And we use 7,418 movies with rating that is not fewer than 7.0. In order to experiment, we provide Top-5, Top-10 and Top-20 recommended movies to user, and then users choose interested movies. The result of experiment is that average number of to choose interested movie are 2.1 in Top-5, 3.35 in Top-10, 6.35 in Top-20. It is better than results that are yielded by for each metadata.

• Journal of the Korean Applied Science and Technology
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• v.37 no.3
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• pp.473-483
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• 2020

This paper proposes a tourism recommendation system in intelligent cloud environment using information of tourist behavior applied with perceived value. This proposed system applied tourist information and empirical analysis information that reflected the perceptual value of tourists in their behavior to the tourism recommendation system using wide and deep learning technology. This proposal system was applied to the tourism recommendation system by collecting and analyzing various tourist information that can be collected and analyzing the values that tourists were usually aware of and the intentions of people's behavior. It provides empirical information by analyzing and mapping the association of tourism information, perceived value and behavior to tourism platforms in various fields that have been used. In addition, the tourism recommendation system using wide and deep learning technology, which can achieve both memorization and generalization in one model by learning linear model components and neural only components together, and the method of pipeline operation was presented. As a result of applying wide and deep learning model, the recommendation system presented in this paper showed that the app subscription rate on the visiting page of the tourism-related app store increased by 3.9% compared to the control group, and the other 1% group applied a model using only the same variables and only the deep side of the neural network structure, resulting in a 1% increase in subscription rate compared to the model using only the deep side. In addition, by measuring the area (AUC) below the receiver operating characteristic curve for the dataset, offline AUC was also derived that the wide-and-deep learning model was somewhat higher, but more influential in online traffic.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.12
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• pp.2871-2877
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• 2015

Shopping patterns are changing with the emergence of SNS. Recently, it is also interested in providing the information based on the users' needs. Generally, the provided information is obtained from the history of users' simple browsing. Best selling hot item list is also provided in order to reflect the preferences of public users. However, the provided information is irrelevant to an individual preference. In this paper, we propose a method to utilize inner and outer SNS for analyzing public preferences about goods which are interested by individual users. The inner analyzing module collects and analyzes the preferences of community members about two goods designated by individual users. The outer analyzing module supports to analyze public preferences by using the tweeter SNS. The results of implementation show that it is possible to recommend goods based on the individual users' preferences unlike the existing shopping mall.

• Journal of Digital Convergence
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• v.15 no.4
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• pp.191-198
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• 2017

It is needed to support intelligent customized health information service for user convenience in PHR based Personal Health Care Service Platform. In this paper, we specify an ontology-based health data model for Personal Health Care Service Platform. We also design a knowledge search algorithm that can be used to figure out similar health record by applying machine learning and data mining techniques. Axis-based mining algorithm, which we proposed, can be performed based on axis-attributes in order to improve relevance of knowledge exploration and to provide efficient search time by reducing the size of candidate item set. And K-Nearest Neighbor algorithm is used to perform to do grouping users byaccording to the similarity of the user profile. These algorithms improves the efficiency of customized information exploration according to the user 's disease and health condition. It can be useful to apply the proposed algorithm to a process of inference in the Personal Health Care Service Platform and makes it possible to recommend customized health information to the user. It is useful for people to manage smart health care in aging society.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.6
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• pp.2819-2827
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• 2013

The purpose of his study was to analyze the environmental and the medical factors of prematurely discharged patients in a rehabilitation hospital. The subjects were 107 inpatients. The data were collected using self-report questionnaire and analyzed using the SPSS Win 19.0 program. In result, Firstly, general selection of hospitals was the 'Recommendation' 35.5%, Environmental dissatisfaction factors are Hospital facilities 37.4%. Hospital choice is the highest 'Acquaintances' 23.4%. Second, the lowest group about professionalism, kindness, and description of explanation on satisfaction in survey is group of care workers for the sick. The highest group is physical and occupational therapist. Third, the satisfaction regarding medical procedures of administrative work, waiting time and medical expenses is the highest normal. Hospital facilities in one of the highest factors in environmental dissatisfaction is can be considered as an element of patient departure because of not much number of nervous special hospitals. Medical dissatisfaction factors is the lowest satisfaction of care worker even if those spend much time. this is the focus leaving hospital. Therefore, factors care workers on service satisfaction and dissatisfaction in analysis and problem solving is considered that the need to find ways to improve the quality of service care workers.