A Network Approach to Derive Product Relations and Analyze Topological Characteristics

Hyea Kyeong Kim,Qiu Yi Chen,Jae Kyeong Kim 한국지능정보시스템학회 2009 한국지능정보시스템학회 학술대회논문집 Vol.2009 No.11

In this study, we propose a co-purchased product network to analyze the relation among all products. Compared to market basket analysis, which focuses on the transactionleveled relation between products, network-based analysis focuses on network-leveled global view point of the relation between products. Two kinds of product networks, market basket network and co-purchased product network are constructed, and comparatively evaluated to analyze the topological characteristics and structure of two networks. The extended use of market basket analysis and networkleveled analysis are expected to be used in more effective and efficient personalized services, such as cross selling, up selling, and personalized product display utilizing the deep relation between products.

A New Item Recommendation Procedure Using Preference Boundary

Hyea Kyeong Kim,Moon Kyoung Jang,Jae Kyeong Kim,Yoon Ho Cho 한국경영정보학회 2010 Asia Pacific Journal of Information Systems Vol.20 No.1

Local Scoring Model for Recommender Network

Hyea Kyeong Kim,Jae Kyeong Kim 한국경영정보학회 2009 한국경영정보학회 학술대회논문집 Vol.2009 No.-

A Min-Max Distance-based Preference Boundary Applied for Recommending New Items

Hyea Kyeong Kim,Min Kyu Jung,Moon Kyoung Jang,Jae Kyeong Kim 한국경영정보학회 2011 한국경영정보학회 학술대회논문집 Vol.2011 No.06

A New Item Recommendation Procedure Using Preference Boundary

Kim, Hyea-Kyeong,Jang, Moon-Kyoung,Kim, Jae-Kyeong,Cho, Yoon-Ho The Korea Society of Management Information System 2010 Asia Pacific Journal of Information Systems Vol.20 No.1

Lately, in consumers' markets the number of new items is rapidly increasing at an overwhelming rate while consumers have limited access to information about those new products in making a sensible, well-informed purchase. Therefore, item providers and customers need a system which recommends right items to right customers. Also, whenever new items are released, for instance, the recommender system specializing in new items can help item providers locate and identify potential customers. Currently, new items are being added to an existing system without being specially noted to consumers, making it difficult for consumers to identify and evaluate new products introduced in the markets. Most of previous approaches for recommender systems have to rely on the usage history of customers. For new items, this content-based (CB) approach is simply not available for the system to recommend those new items to potential consumers. Although collaborative filtering (CF) approach is not directly applicable to solve the new item problem, it would be a good idea to use the basic principle of CF which identifies similar customers, i,e. neighbors, and recommend items to those customers who have liked the similar items in the past. This research aims to suggest a hybrid recommendation procedure based on the preference boundary of target customer. We suggest the hybrid recommendation procedure using the preference boundary in the feature space for recommending new items only. The basic principle is that if a new item belongs within the preference boundary of a target customer, then it is evaluated to be preferred by the customer. Customers' preferences and characteristics of items including new items are represented in a feature space, and the scope or boundary of the target customer's preference is extended to those of neighbors'. The new item recommendation procedure consists of three steps. The first step is analyzing the profile of items, which are represented as k-dimensional feature values. The second step is to determine the representative point of the target customer's preference boundary, the centroid, based on a personal information set. To determine the centroid of preference boundary of a target customer, three algorithms are developed in this research: one is using the centroid of a target customer only (TC), the other is using centroid of a (dummy) big target customer that is composed of a target customer and his/her neighbors (BC), and another is using centroids of a target customer and his/her neighbors (NC). The third step is to determine the range of the preference boundary, the radius. The suggested algorithm Is using the average distance (AD) between the centroid and all purchased items. We test whether the CF-based approach to determine the centroid of the preference boundary improves the recommendation quality or not. For this purpose, we develop two hybrid algorithms, BC and NC, which use neighbors when deciding centroid of the preference boundary. To test the validity of hybrid algorithms, BC and NC, we developed CB-algorithm, TC, which uses target customers only. We measured effectiveness scores of suggested algorithms and compared them through a series of experiments with a set of real mobile image transaction data. We spilt the period between 1st June 2004 and 31st July and the period between 1st August and 31st August 2004 as a training set and a test set, respectively. The training set Is used to make the preference boundary, and the test set is used to evaluate the performance of the suggested hybrid recommendation procedure. The main aim of this research Is to compare the hybrid recommendation algorithm with the CB algorithm. To evaluate the performance of each algorithm, we compare the purchased new item list in test period with the recommended item list which is recommended by suggested algorithms. So we employ the evaluation metric to hit the ratio for evaluating our algorithms. The hit ratio is defined as the ra

사회 네트워크를 이용한 사용자 기반 유헬스케어 서비스 추천 시스템 개발

김혜경(Hyea Kyeong Kim),최일영(Il Young Choi),하기목(Ki Mok Ha),김재경(Jae Kyeong Kim) 한국지능정보시스템학회 2010 지능정보연구 Vol.16 No.3

As rapid progress of population aging and strong interest in health, the demand for new healthcare service is increasing. Until now healthcare service has provided post treatment by face-to-face manner. But according to related researches, proactive treatment is resulted to be more effective for preventing diseases. Particularly, the existing healthcare services have limitations in preventing and managing metabolic syndrome such a lifestyle disease, because the cause of metabolic syndrome is related to life habit. As the advent of ubiquitous technology, patients with the metabolic syndrome can improve life habit such as poor eating habits and physical inactivity without the constraints of time and space through u-healthcare service. Therefore, lots of researches for u-healthcare service focus on providing the personalized healthcare service for preventing and managing metabolic syndrome. For example, Kim et al.(2010) have proposed a healthcare model for providing the customized calories and rates of nutrition factors by analyzing the user’s preference in foods. Lee et al.(2010) have suggested the customized diet recommendation service considering the basic information, vital signs, family history of diseases and food preferences to prevent and manage coronary heart disease. And, Kim and Han(2004) have demonstrated that the web-based nutrition counseling has effects on food intake and lipids of patients with hyperlipidemia. However, the existing researches for u-healthcare service focus on providing the predefined one-way u-healthcare service. Thus, users have a tendency to easily lose interest in improving life habit. To solve such a problem of u-healthcare service, this research suggests a u-healthcare recommender system which is based on collaborative filtering principle and social network. This research follows the principle of collaborative filtering, but preserves local networks (consisting of small group of similar neighbors) for target users to recommend context aware healthcare services. Our research is consisted of the following five steps. In the first step, user profile is created using the usage history data for improvement in life habit. And then, a set of users known as neighbors is formed by the degree of similarity between the users, which is calculated by Pearson correlation coefficient. In the second step, the target user obtains service information from his/her neighbors. In the third step, recommendation list of top-N service is generated for the target user. Making the list, we use the multi-filtering based on user’s psychological context information and body mass index (BMI) information for the detailed recommendation. In the fourth step, the personal information, which is the history of the usage service, is updated when the target user uses the recommended service. In the final step, a social network is reformed to continually provide qualified recommendation. For example, the neighbors may be excluded from the social network if the target user doesn’t like the recommendation list received from them. That is, this step updates each user’s neighbors locally, so maintains the updated local neighbors always to give context aware recommendation in real time. The characteristics of our research as follows. First, we develop the u-healthcare recommender system for improving life habit such as poor eating habits and physical inactivity. Second, the proposed recommender system uses autonomous collaboration, which enables users to prevent dropping and not to lose user’s interest in improving life habit. Third, the reformation of the social network is automated to maintain the quality of recommendation. Finally, this research has implemented a mobile prototype system using JAVA and Microsoft Access2007 to recommend the prescribed foods and exercises for chronic disease prevention, which are provided by A university medical center. This research intends to prevent diseases such as chronic illne

A New Item Recommendation Procedure Using Preference Boundary

Jae Kyeong Kim,Moon Kyoung Jang,Hyea Kyeong Kim,Yoon Ho Cho 한국경영정보학회 2009 한국경영정보학회 학술대회논문집 Vol.2009 No.-

백화점 거래 데이터를 이용한 상품 네트워크 연구

김혜경(Hyea-Kyeong Kim),김재경(Jae-Kyeong Kim),Chen Qiu Yi 한국지능정보시스템학회 2009 지능정보연구 Vol.15 No.4

We construct product networks from the retail transaction dataset of an off?line department store. In the product networks, nodes are products, and an edge connecting two products represents the existence of co?purchases by a customer. We measure the quantities frequently used for characterizing network structures, such as the degree centrality, the closeness centrality, the betweenness centrality and the centralization. Using the quantities, gender, age, seasonal, and regional differences of the product networks were analyzed and network characteristics of each product category containing each product node were derived. Lastly, we analyze the correlations among the three centrality quantities and draw a marketing strategy for the cross?selling.

재래시장 활성화를 위한 u-Market 시스템 아키텍처 설계 및 시스템 개발

김재경(Jae Kyeong Kim),최일영(Il Young Choi),채경희(Kyung Hee Chae),김혜경(Hyea Kyeong Kim),지용구(Yong Gu Ji),정혜정(Hye Jung Jung) 한국지능정보시스템학회 2008 지능정보연구 Vol.14 No.2

Traditional market which is characterized by the folksy retailing market has lost its competitiveness rapidly due to the emergence of the Internet and the change of customer’s purchasing behavior. The recession of the traditional market contracts the regional economy. We suggest a u-Market, a traditional market with ubiquitous computing capability, to revitalize traditional market. The suggested u-Market system applies ubiquitous computing technologies characterized by communications between customers and objects without limitations of time and location. The proposed u-Market system offers location information and specific contents of traditional market to customers. Furthermore, u-Market system recommends the store and product list that customers are likely to visit and purchase based on their contexts, so they can save their time and effort to search the products or contents.

블로그 인텔리전스

김재경(Jae Kyeong Kim),김혜경(Hyea Kyeong Kim),오혁(Hyouk O) 한국IT서비스학회 2008 한국IT서비스학회지 Vol.7 No.3

  The rapid growth of blog has caused information overload where bloggers in the virtual community space are no longer able to effectively choose the blogs they are exposed to. Recommender systems have been widely advocated as a way of coping with the problem of information overload in e-business environment. Collaborative Filtering (CF) is the most successful recommendation method to date and used in many of the recommender systems. In this research, we propose a CF-based recommender system for bloggers to find their similar bloggers or preferable virtual community without burdensome search effort. For such a purpose, we apply the “Interest Value” to CF recommender systems. The Interest Value is the quantity value about users’ transaction data in virtual community, and can measure the opinion of users accurately. Based on the Interest Value, the neighborhood group is generated, and virtual community list is recommended using the Community Likeness Score (ClS). Our experimental results upon real data of Korean Blog site show that the methodology is capable of dealing with the information overload issue in virtual community space. And Interest Value is proved to have the potential to meet the challenge of recommendation methodologies in virtual community space.