• The KIPS Transactions:PartD
• /
• v.13D no.7 s.110
• /
• pp.1027-1038
• /
• 2006

There are many technical problems in the recommendation system based on very large database(VLDB). So, it is necessary to study the recommendation system' structure and the data-mining technique suitable for the large scale Internet shopping mail. Thus we design and implement the product recommendation system using k-means clustering algorithm and sequential pattern technique which can be used in large scale Internet shopping mall. This paper processes user information by batch processing, defines the various categories by hierarchical structure, and uses a sequential pattern mining technique for the search engine. For predictive modeling and experiment, we use the real data(user's interest and preference of given category) extracted from log file of the major Internet shopping mall in Korea during 30 days. And we define PRP(Predictive Recommend Precision), PRR(Predictive Recommend Recall), and PF1(Predictive Factor One-measure) for evaluation. In the result of experiments, the best recommendation time and the best learning time of our system are much as O(N) and the values of measures are very excellent.

• Journal of Digital Convergence
• /
• v.19 no.2
• /
• pp.299-305
• /
• 2021

This study examined to identify the factors influencing on inpatients' satisfaction with hospitalization and willingness to recommend hospital to others. Data from the 2018 National Patient Experience Survey were used for the analysis. Of the 593 patients experiencing inpatient services, multivariate linear regression analysis was conducted to explore the factors such as physician care, nursing care, facility and administrative support, and healthcare system on inpatients' satisfaction after controlling for their socio-demographic and health related factors. The study found that physician care, nursing care, administrative support and healthcare system were related to inpatients' satisfaction and willingness to recommend hospital. To improve inpatients' satisfaction, it is necessary to improve healthcare professionals' overall patient-centerdness attitude, user friendly hospital facilities and administrative support services and efforts to trust and satisfaction on healthcare system from the patients' perspectives.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2005.05a
• /
• pp.131-138
• /
• 2005

Recommender systems have been widely advocated as a way of coping with the problem of information overload in e-business environment. Most of the existing recommender systems focused on what kind of items to recommend, although when to recommend to the target customer considering their context is an important issue. Even right item might be a spam advertisement or wrong recommendation for the customer if it can not be recommended at the right context. It is particularly important for recommendations where the user's context is changing rapidly, such as in both handheld and ubiquitous computing environment. Therefore, we propose CARS (Context-Aware Recommender System) based on CBR and context-awareness for ubiquitous computing environment. CBR is used to generate a target customer class and proper context. Context-awareness is used to gather suer context information from sensors, networks, device status, user profiles, and other sources. An illustrative case example is suggested to explain the procedure of CARS.

• 한국정보통신설비학회:학술대회논문집
• /
• 2005.08a
• /
• pp.109-112
• /
• 2005

In this thesis, we recommend the method for VOD service traffic modeling. By the analysis of service traffic, we verify that the process of VOD service is the POISSON process. So it is important to determine the probability of the user's existence in the system. But, because it is difficult to measure, we recommend proper using of arrival rate and service rate.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2020.07a
• /
• pp.385-386
• /
• 2020

In this paper, we propose a collaborative filtering recommendation algorithm based on the LDA2Vec topic model. By extracting and analyzing the article's content, calculate their semantic similarity then combine the traditional collaborative filtering algorithm to recommend. This approach may promote the system's recommend accuracy.

• Journal of Information Technology Applications and Management
• /
• v.24 no.1
• /
• pp.33-43
• /
• 2017

The content curation service through big data analysis is receiving great attention in various content fields, such as film, game, music, and book. This service recommends personalized contents to the corresponding user based on user's preferences. The existing book curation systems recommended books to users by using bibliographic citation, user profile or user log data. However, these systems are difficult to recommend books related to character names or spatio-temporal information in text contents. Therefore, in this paper, we suggest a personalized book curation system based on integrated mining of a book. The proposed system consists of mining system, recommendation system, and visualization system. The mining system analyzes book text, user information or profile, and SNS data. The recommendation system recommends personalized books for users based on the analysed data in the mining system. This system can recommend related books using based on book keywords even if there is no user information like new customer. The visualization system visualizes book bibliographic information, mining data such as keyword, characters, character relations, and book recommendation results. In addition, this paper also includes the design and implementation of the proposed mining and recommendation module in the system. The proposed system is expected to broaden users' selection of books and encourage balanced consumption of book contents.

• The KIPS Transactions:PartB
• /
• v.15B no.4
• /
• pp.347-354
• /
• 2008

In this paper, we propose a method for personalizing and intelligence of speech Web. The proposed system records information that was demanded in the past as a transaction, explores association rules from those transactions, and discovers itemsets from frequent requests. This method is to recommend relevant information, based on frequent itemsets, to users who have similar inclinations to previous users. As a result of experimenting and implementation of proposed system for verification, we confirmed that the proposed system can recommend previously frequently requested information as relevant information.

• The Journal of the Korea Contents Association
• /
• v.7 no.4
• /
• pp.66-73
• /
• 2007

Internet is a part of our common life and tremendous information is cumulated. In these trends, the personalization becomes a very important technology which could find exact information to present users. Previous personalized services use content based filtering which is able to recommend by analyzing the content and collaborative filtering which is able to recommend contents according to preference of users group. But, collaborative filtering needs the evaluation of some amount of data. Also, It cannot reflect all data of users because it recommends items based on data of some users who have similar inclination. Therefore, we need a new recommendation method which can recommend prefer items without preference data of users. In this paper, we proposed the apparel coordination system using personalized preference on the semantic web. This paper provides the results which this system can reduce the searching time and advance the customer satisfaction measurement according to user's feedback to system.

• Journal of the Korea Society of Computer and Information
• /
• v.12 no.6
• /
• pp.207-212
• /
• 2007

Recently, requriements that people want to receive various information related with fashion are increasing. A lot of internet shopping malls and corporations provide information about fashion. However, those systems do not give enough information about fashion. To solve these problems, the paper provides the recommend technique for providing complex fashion information on mobile devices. The providing system implements fasion ontology by using XML. The XML ontology has dewey number as an attribute. The recommend technique uses this number and find LCA(Lowest Common Ancestor) on the fashion ontology. Then those child nodes under the LCA are recommended as related information. The results are displayed on the mobile browser. The system provides function for taking a picture or movie of fashion contents. Those movies and pictures are UCC(User Created Content)s. The system is a novel system that can recommend complex fasion information on mobile devices.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2005.05a
• /
• pp.232-239
• /
• 2005

Collaborative-filtering-enabled Web sites that recommend books, CDs, movies, and so on, have become very popular on the Internet. Such sites recommend items to a user on the basis of the opinions of other users with similar tastes. This paper discuss an approach to collaborative filtering based on the Simple Bayesian and apply this model to two variants of the collaborative filtering. One is user-based collaborative filtering, which makes predictions based on the users' similarities. The other is item-based collaborative filtering which makes predictions based on the items' similarities. To evaluate the proposed algorithms, this paper used a database of movie recommendations. Empirical results show that the proposed Bayesian approaches outperform typical correlation-based collaborative filtering algorithms.