• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.113-127
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• 2016

Among the techniques for recommendation, collaborative filtering (CF) is commonly recognized to be the most effective for implementing recommender systems. Until now, CF has been popularly studied and adopted in both academic and real-world applications. The basic idea of CF is to create recommendation results by finding correlations between users of a recommendation system. CF system compares users based on how similar they are, and recommend products to users by using other like-minded people's results of evaluation for each product. Thus, it is very important to compute evaluation similarities among users in CF because the recommendation quality depends on it. Typical CF uses user's explicit numeric ratings of items (i.e. quantitative information) when computing the similarities among users in CF. In other words, user's numeric ratings have been a sole source of user preference information in traditional CF. However, user ratings are unable to fully reflect user's actual preferences from time to time. According to several studies, users may more actively accommodate recommendation of reliable others when purchasing goods. Thus, trust relationship can be regarded as the informative source for identifying user's preference with accuracy. Under this background, we propose a new hybrid recommender system that fuses CF and social network analysis (SNA). The proposed system adopts the recommendation algorithm that additionally reflect the result analyzed by SNA. In detail, our proposed system is based on conventional memory-based CF, but it is designed to use both user's numeric ratings and trust relationship information between users when calculating user similarities. For this, our system creates and uses not only user-item rating matrix, but also user-to-user trust network. As the methods for calculating user similarity between users, we proposed two alternatives - one is algorithm calculating the degree of similarity between users by utilizing in-degree and out-degree centrality, which are the indices representing the central location in the social network. We named these approaches as 'Trust CF - All' and 'Trust CF - Conditional'. The other alternative is the algorithm reflecting a neighbor's score higher when a target user trusts the neighbor directly or indirectly. The direct or indirect trust relationship can be identified by searching trust network of users. In this study, we call this approach 'Trust CF - Search'. To validate the applicability of the proposed system, we used experimental data provided by LibRec that crawled from the entire FilmTrust website. It consists of ratings of movies and trust relationship network indicating who to trust between users. The experimental system was implemented using Microsoft Visual Basic for Applications (VBA) and UCINET 6. To examine the effectiveness of the proposed system, we compared the performance of our proposed method with one of conventional CF system. The performances of recommender system were evaluated by using average MAE (mean absolute error). The analysis results confirmed that in case of applying without conditions the in-degree centrality index of trusted network of users(i.e. Trust CF - All), the accuracy (MAE = 0.565134) was lower than conventional CF (MAE = 0.564966). And, in case of applying the in-degree centrality index only to the users with the out-degree centrality above a certain threshold value(i.e. Trust CF - Conditional), the proposed system improved the accuracy a little (MAE = 0.564909) compared to traditional CF. However, the algorithm searching based on the trusted network of users (i.e. Trust CF - Search) was found to show the best performance (MAE = 0.564846). And the result from paired samples t-test presented that Trust CF - Search outperformed conventional CF with 10% statistical significance level. Our study sheds a light on the application of user's trust relationship network information for facilitating electronic commerce by recommending proper items to users.

• Journal of KIISE
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• v.44 no.10
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• pp.1087-1093
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• 2017

Educating foreigners in Korean language is attracting increasing attention with the growing number of foreigners who want to learn Korean or want to reside in Korea. Existing spell checkers mostly focus on native Korean speakers, so they are inappropriate for foreigners. In this paper, we propose a correction method for the Korean language that reflects the contextual characteristics of Korean and writing characteristics of foreigners. Our method can extract frequently used expressions by Koreans by constructing syllable reverse-index for eojeol bi-gram extracted from corpus as correction candidates, and generate ranked Korean corrections for foreigners with upgraded edit distance calculation. Our system provides a user interface based on keyboard hooking, so a user can easily use the correction system along with other applications. Our system improves the detection rate for foreign language users by about 45% compared to other systems in foreign language writing environments. This will help foreign users to judge and correct their own writing errors.

• 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.

• The Journal of the Korea Contents Association
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• v.9 no.10
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• pp.389-397
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• 2009

The objectives of this study are to analyze the effects of major factors influencing cruise ship passengers' satisfaction with a port of call on overall satisfaction with it and the differences in satisfaction with the domestic port of calls. To achieve these objectives, as a conceptual framework of the study, cruise tourism and tourist satisfaction were reviewed, and the empirical studies on cruise ship passengers's perceptions toward the Korean port of calls were conducted. According to the findings of this study, the following suggestions were presented to the local governments with the port of calls. The local governments are required to improve the food services provided around the port of call areas, and in addition, in case of Cheju, improvement of tourism information service which is verified as the most significant factor affecting passenger's overall satisfaction with the port of call is needed as well.

• Culinary science and hospitality research
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• v.19 no.4
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• pp.268-278
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• 2013

The purpose of this research is to examine the effects of selection attributes of medicinal food on customer satisfaction and purchase attitude in Jinju area. A survey was conducted to 300 people who live in Jinju area, and 252 completed copies of questionnaire was returned. Statistical package 'SPSS WIN 20.0' was used to analyze the sample data, and the result of the analysis is as follows. First, for the hypothesis, 'selection attributes of medicinal food have a significant effect on satisfaction,' food quality, health food and services have a significant effect on customer satisfaction. Second, customer satisfaction with medicinal food has a significant effect on revisit. Third, customer satisfaction leads to recommendation to others. In conclusion, this research shows that medicinal food restaurants in Jinju area should provide healthy food menu, high-quality food and high-class services, which could be effective to promote the specialty of medicinal food restaurants for costumers.

• Journal of Gifted/Talented Education
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• v.22 no.2
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• pp.353-370
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• 2012

The observation and recommendation system was recently introduced for selecting gifted-students in science, and it has required to arrange the reliable and valid selection criteria that could identify the high potential competency of them. In this study, we explored the competencies that could help to discriminate gifted-students' inner properties, and also developed the dictionary based on them. The competencies were extracted from the proven previous competency dictionaries/models and examined by the structured survey and the focus group interview in order to ascertain the competencies of the science-gifted students. The results revealed that there were two competency clusters such as cognitive and affective domains. The cognitive cluster consisted of 6 competencies as follows: goal suggestion, planning, information collection and analysis, problem solving, higher-order thinking, and expertise and self-development competency. The affective cluster consisted of 3 competencies: confidence, achievement orientation, and curiosity competency. The dictionary categorized by the names, definitions, key elements, and behavioral indicators and their levels of the derived competencies was developed. Findings were expected to provide the implications on the selection criteria of the potential science-gifted students through the observation and recommendation system.

• The Journal of the Korea Contents Association
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• v.9 no.6
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• pp.357-369
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• 2009

With the development and generalization of internet and information technology, airlines has tried to reduce their business expenses and commissions to travel agencies and enhance service qualities through service automation and simplification, such as internet booking and ticketing, self check-in, in-flight internet and RFID for checked baggage. The statistical techniques conducted for this empirical analysis are frequency analysis, reliability analysis, factor analysis, confirmatory factor analysis and multiple regression analysis. This research has tried to examine factors of airline e-services that influence on recommendation re-purchase intention and satisfaction. Results has found that only on-line reservation and ticketing factor had significant effect for recommendation and re-purchase intention and all e-service factors produced significant effect to total satisfaction. It was also recommend that airlines have to provide easy and more familiar e-service system to their passengers to deliver better services.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.137-148
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• 2014

Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used