• Journal of the Korea Society of Computer and Information
• /
• v.15 no.11
• /
• pp.205-213
• /
• 2010

This study uses the FP-tree algorithm, one of the mining techniques. This study is an attempt to suggest a new recommended system using a modified FP-tree algorithm which yields an association rule based on frequent 2-itemsets extracted from the transaction database. The modified recommended system consists of a pre-processing module, a learning module, a recommendation module and an evaluation module. The study first makes an assessment of the modified recommended system with respect to the precision rate, recall rate, F-measure, success rate, and recommending time. Then, the efficiency of the system is compared against other recommended systems utilizing the sequential pattern mining. When compared with other recommended systems utilizing the sequential pattern mining, the modified recommended system exhibits 5 times more efficiency in learning, and 20% improvement in the recommending capacity. This result proves that the modified system has more validity than recommended systems utilizing the sequential pattern mining.

• Journal of Korea Multimedia Society
• /
• v.20 no.9
• /
• pp.1541-1550
• /
• 2017

The increasing number of multimedia content offered to the user demands content recommendation. In this paper, we propose a system for recommending content related to the content that user is watching. In the proposed system, relationship information between content is generated using relationship information between representative keywords of content. Relationship information between keywords is generated by analyzing keyword collocation frequencies in Internet news corpus. In order to handle big corpus data, we design an architecture that consists of a distributed search engine and a distributed data processing engine. Furthermore, we store relationship information between keywords and relationship information between keywords and content in NoSQL to handle big relationship data. Because the query optimizer of NoSQL is not as well developed as RDBMS, we propose query optimization techniques to efficiently process complex queries for recommendation. Experimental results show that the performance is improved by up to 69 times by using the proposed techniques, especially when the number of requested related keywords is small.

• The Journal of the Korea Contents Association
• /
• v.18 no.1
• /
• pp.64-75
• /
• 2018

With the development of location-aware technologies and the activation of smart phones, location based social networks(LBSN) have been activated to allow people to easily share their location. In particular, studies on recommending the location of user interests by using the user check-in function in LBSN have been actively conducted. In this paper, we propose a location recommendation scheme considering time and user activities in LBSN. The proposed scheme considers user preference changes over time, local experts, and user interest in rare places. In other words, it uses the check-in history over time and distinguishes the user activity area to identify local experts. It also considers a rare place to give a weight to the user preferred place. It is shown through various performance evaluations that the proposed scheme outperforms the existing schemes.

• The Journal of the Korea Contents Association
• /
• v.5 no.6
• /
• pp.109-119
• /
• 2005

The Internet is widely used by general people and the use of Internet is spread to all industrial fields. Especially, cyber education fields have been changed a lot with the Internet application development. One of them is the field of consultation for university admission. As for the business of university admission, there were two ways applicants handed in their applications directly to school which they applied to and to each place to receive applications or sent them through FAX. Recently, highlighted is the Internet environment to receive the application for admission which integrated organically the two ways. In this thesis, I designed and implemented on-line admission consulting system using of multi-agent. The examines can apply for safely and according to their conviction by recommending the university course suitable for their academic aptitude and scores with test KSAT(Scholastic Aptitude Test Administered by the Korean Ministry) and a university grade report on students' record using intelligent multi-agents and through a university course recommending wizard in the process of choosing it.

• Journal of Korean Public Health Nursing
• /
• v.19 no.1
• /
• pp.129-138
• /
• 2005

As the patients who need to undergo liver transplant operation continues to grow. the number of livers that are donated can not keep pace with the demand. With the development of surgery skills, the necessity for operations from living donors is increasing. Nevertheless, satisfactory research has been conducted on the factors which generally affect the living donors. In this article. therefore. researchers focused on the factors which generally affect the donating liver donor in order to design a plan for recommending liver donation from living donors. The subjects were 91 living liver donors in C university hospital from October 1. 2000 to December 31. 2003. The results on the uncertainty of living donor, by test sheet. were analyzed with SAS program. The final results were as follows: 1. The uncertainty of the living donors was 51.54 marks per full credit 100. 2. The factor with the greatest effect on donation was the possibility of survival of the donor, followed by the admission period. marriage status and age. In recommending the living donation, the rate of donor survival after the operation was 5.2 times higher than death, 5.2 times higher when the admission period was under 20 days. 5.0 times higher when married. and 27.3 times higher when the family-related donation was very active at the age of 20s than in the 50s. These results suggest that all medical staffs should care for living donors with more interest and activity to give them the least complaints in admission and the lowest possibilities for complication. To enhance the survival rate and improve the surgical success rate. on-going monitoring should include regular health-checks. and continual efforts and education should be made to care for the health condition of the living donors after donation.

• Journal of the Korea Society of Computer and Information
• /
• v.18 no.2
• /
• pp.107-116
• /
• 2013

In this paper, we propose a hybrid music recommendation system combining users' listening habits and tag information in a social music site. Most of commercial music recommendation systems recommend music items based on the number of plays and explicit ratings of a song. However, the approach has some difficulties in recommending new items with only a few ratings or recommending items to new users with little information. To resolve the problem, we use tag information which is generated by collaborative tagging. According to the meaning of tags, a weighted value is assigned as the score of a tag of an music item. By combining the score of tags and the number of plays, user profiles are created and collaborative filtering algorithm is executed. For performance evaluation, precision, recall, and F-measure are calculated using the listening habit-based recommendation, the tag score-based recommendation, and the hybrid recommendation, respectively. Our experiments show that the hybrid recommendation system outperforms the other two approaches.

• Journal of Broadcast Engineering
• /
• v.24 no.6
• /
• pp.1013-1023
• /
• 2019

In content consumption environment with various live TV channels, VoD contents and web contents, recommendation service is now a necessity, not an option. Currently, various kinds of recommendation services are provided in the OTT service or the IPTV service, such as recommending popular contents or recommending related contents which similar to the content watched by the user. However, in the case of a content viewing environment through TV or IPTV which shares one TV and a TV set-top box, it is difficult to recommend proper content to a specific user because one or more usage histories are accumulated in one subscription information. To solve this problem, this paper interprets the concept of family as {user, time}, extends the existing recommendation relationship defined as {user, content} to {user, time, content} and proposes a method based on deep learning algorithm. Through the proposed method, we evaluate the recommendation performance qualitatively and quantitatively, and verify that our proposed model is improved in recommendation accuracy compared with the conventional method.

• Journal of the Institute of Convergence Signal Processing
• /
• v.20 no.1
• /
• pp.31-36
• /
• 2019

A chatbot is an important area of mobile service, which understands informal data of a user as a conversational form and provides a customized service information for user. However, there is still a lack of a service way to fully understand the user's natural language typed query dialogue. Therefore, in this paper, we extract meaningful words, such a region, a food category, and a restaurant name from user's dialogue sentences for recommending a restaurant. and by comparing the extracted words against the contents of the knowledge database that is built from the hashtag for recommending a restaurant in SNS, and provides user target information having statistically much the word-similarity. In order to evaluate the performance of the restaurant recommendation chatbot system implemented in this paper, we measured the accessibility of various user query information by constructing a web-based mobile environment. As a results by comparing a previous similar system, our chabot is reduced by 37.2% and 73.3% with respect to the touch-count and the cutaway-count respectively.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.9
• /
• pp.1293-1304
• /
• 2022

The personalization recommendation system means analyzing each individual's interests or preferences and recommending information or products accordingly. These personalized recommendations can reduce the time consumers spend searching for information by accessing the products they need more quickly, and companies can increase corporate profits by recommending appropriate products that meet their needs. In this study, products are recommended to consumers using collaborative filtering, matrix factorization, and deep learning, which are representative personalization recommendation techniques. To this end, the data set after purchasing shopping mall products, which is raw data, is pre-processed in the form of transmitting the data set to the input of the recommended system, and the pre-processed data set is analyzed from various angles. In addition, each model performs verification and performance comparison on the recommended results, and explores the model with optimal performance, suggesting which model should be used when building the recommendation system at the mall.

• Research in Community and Public Health Nursing
• /
• v.33 no.2
• /
• pp.164-174
• /
• 2022

Purpose: The purpose of this study is to examine the effects of the rural elderly suicide literacy level upon suicide stigma and coping advice with suicidal crises (recommending professional help for a suicidal person). In particular, this study investigates the role of cultural norms (perceived social expectations for the experience of negative emotions) on suicide stigma and coping ability. Methods: A survey was conducted addressing elderly people (N=119) living in rural areas. Regression analysis using SPSS PROCESS macro was used to examine the relationships among the key variables. Results: Participants with higher suicide literacy showed lower suicide stigma, and this perception had a significant effect on enhancing their coping advice with suicidal crises. Also, perceived social expectations significantly influenced the relationship between suicide stigma and coping advice. With lower levels of social expectations, the mediating effect of suicide stigma on the relationship between suicide literacy and recommending professional help did not exist whereas the indirect effect was significant when it pertained to high levels of social expectations. Conclusion: This result signifies that suicide stigma serves as a barrier deterring Koreans from reaching out for professional help regarding their mental health. Moreover, these findings underscore the importance of cultural psychological factors such as perceived social expectations in terms of developing suicide prevention strategies.