• Journal of KIISE:Software and Applications
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• v.37 no.6
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• pp.470-479
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• 2010

In relation to the extraction of opinion words, it may be difficult to directly apply most of the methods suggested in existing English studies to the Korean language. Additionally, the manual method suggested by studies in Korea poses a problem with the extraction of opinion words in that it takes a long time. In addition, English thesaurus-based extraction of Korean opinion words leaves a challenge to reconsider the deterioration of precision attributed to the one to one mismatching between Korean and English words. Studies based on Korean phrase analyzers may potentially fail due to the fact that they select opinion words with a low level of frequency. Therefore, this study will suggest the k-Structure (k=5 or 8) method, which may possibly improve the precision while mutually complementing existing studies in Korea, in automatically extracting opinion words from a simple sentence in a given Korean product review. A simple sentence is defined to be composed of at least 3 words, i.e., a sentence including an opinion word in ${\pm}2$ distance from the attribute name (e.g., the 'battery' of a camera) of a evaluated product (e.g., a 'camera'). In the performance experiment, the precision of those opinion words for 8 previously given attribute names were automatically extracted and estimated for 1,868 product reviews collected from major domestic shopping malls, by using k-Structure. The results showed that k=5 led to a recall of 79.0% and a precision of 87.0%; while k=8 led to a recall of 92.35% and a precision of 89.3%. Also, a test was conducted using PMI-IR (Pointwise Mutual Information - Information Retrieval) out of those methods suggested in English studies, which resulted in a recall of 55% and a precision of 57%.

• Journal of Intelligence and Information Systems
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• v.14 no.4
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• pp.201-216
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• 2008

Nowadays various new items are available, but limitation of searching effort makes it difficult for customers to search new items which they want to purchase. Therefore new item providers and customers need recommendation systems which recommend right items for right customers. In this research, we focus on the new item recommendation issue, and suggest preference boundary- based procedures which extend traditional content-based algorithm. We introduce the concept of preference boundary in a feature space to recommend new items. To find the preference boundary of a target customer, we suggest heuristic algorithms to find the centroid and the radius of preference boundary. To evaluate the performance of suggested procedures, we have conducted several experiments using real mobile transaction data and analyzed their results. Some discussions about our experimental results are also given with a further research area.

• Journal of Korea Multimedia Society
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• v.11 no.2
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• pp.257-268
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• 2008

Multimedia recommendation systems analyze user preferences and recommend items(multimedia contents) to a user by predicting the user's preference for those items. Among various kinds of recommendation methods, collaborative filtering(CF) has been widely used and successfully applied to practical applications. However, collaborative filtering has two inherent problems: data sparseness and the cold-start problems. If there are few known preferences for a user, it is difficult to find many similar users, and therefore the performance of recommendation is degraded. This problem is more serious when a new user is first using the system. In this paper, we propose a method of generating additional feature of users and items into CF to overcome the difficulties caused by sparseness and improve the accuracy of recommendation. In our method, we first generate additional features by using the probability distribution of feature values, then recommend items by applying collaborative filtering on the modified data to include additional features. Several experimental results that show the effectiveness of the proposed method are also presented.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06c
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• pp.397-401
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• 2008

추천시스템은 일반적으로 협동적 필터링이라는 정보 필터링 기술을 사용한다. 협동적 필터링은 유사한 성향을 갖는 다른 고객들이 상품에 대해서 매긴 평가에 기반하기 때문에 고객에게 가장 적합한 유사 이웃들을 적절히 선정해 내는 것이 추천시스템의 예측의 질 향상을 위해서 필요하다. 본 논문에서는 다중 속성 정보를 기반으로 한 다단계 클러스터링을 통한 이웃선정 방법을 제안한다. 이 방법은 대규모 데이터 셋에서 탐색 공간을 줄이기 위해 클러스터링을 수행하여 적절한 이웃 고객들의 집합을 검색하여 추출한다. 이 때, 다중 속성 정보에 따라 단계적으로 클러스터링을 수행함으로써 보다 정제된 고객 집합을 구성할 수 있도록 한다. 본 논문에서는 고객 선호도와 위치 정보 및 아이템의 선호도와 위치 정보를 대표적인 속성 정보로 사용함으로써 모바일 환경에서 보다 정확한 추천이 이루어질 수 있도록 한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.11a
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• pp.525-528
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• 2007

추천시스템은 일반적으로 협동적 필터링이라는 정보 필터링 기술을 사용한다. 협동적 필터링은 유사한 성향을 갖는 다른 고객들이 상품에 대해서 매긴 평가에 기반하기 때문에 고객에게 가장 적합한 유사 이웃들을 적절히 선정해 내는 것이 추천시스템의 예측의 질 향상을 위해서 필요하다. 본 논문에서는 속성 정보를 기반으로 한 다단계 클러스터링을 통한 이웃선정 방법을 제안한다. 이 방법은 대규모 데이터 셋에서 탐색 공간을 줄이기 위해 클러스터링을 수행하여 적절한 이웃 고객들의 집합을 추출한다. 이 때, 속성 정보에 따라 단계적으로 클러스터링을 수행함으로써 보다 정제된 고객집합을 구성할 수 있도록 한다. 본 논문에서는 고객 선호도와 위치 정보를 대표적인 속성 정보로 사용함으로써 모바일 환경에서 보다 정확한 추천이 이루어질 수 있도록 한다.

• Journal of the Korean Geographical Society
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• v.43 no.6
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• pp.938-950
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• 2008

This study was to investigate if there were any significant relationships between cultural destination selection attributes and travel satisfaction according to segmented groups of cultural destination attributes. Survey questionnaire was developed based on the previous study and data were collected from on site survey, which was one of the famous cultural tourism destination in Korea such as Booyoe and Kongjoo. Six dimensions of cultural destination attributes were identified from factor analysis and three different segmented groups were determined from cluster analysis. Then, Multiple regression analysis conducted with six destination attributes as independent variables and one travel satisfaction as dependent variable, while Fisher's Z score for three segmented groups were considered as moderator's variable. The results showed that cultural destination attribute affected respondents' level of travel satisfaction and there was differences among segmented groups in terms of their affecting factors to the travel satisfaction. These findings suggested that there were different segmented groups of cultural destination selection attributes and each group pursued different cultural travel products and services.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.10
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• pp.4328-4336
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• 2011

The aim of this study is to find changes of the attributes that influence the purchase of cut roses during recent five years(2007~2011) and suggest some implications on ways to promote cut roses marketing. For this purpose, a survey was conducted through the Internet among 1,100 randomly chosen people living in Seoul, Inchon and Gyeonggi Province in 2011. A total of 1,023 valid replies were received for the analysis of the survey which was carried out by the subsidiary consulting firm. The survey panels and estimation models to analyze changes of consumers' preference attributes during recent five years are same to them of Kim, et al.(2007). That is, empirical analysis tools such as ordered probit model, multinomial logit model, and conjoint analysis were used according to Kim, et al.(2007). This paper suggests several policy implications to set up the target market of cut roses and marketing mix strategy to specify the best 4P(product, price, place and promotion).

• The Journal of the Korea Contents Association
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• v.20 no.12
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• pp.471-481
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• 2020

This study aims to investigate the relationship between the factors of RMR product selection attributes, brand image, and satisfaction as the interest and frequency of use of RMR products of dining out consumers increase recently. Convenience sampling was used for consumers with experience in using RMR products launched in catering companies and restaurants. The investigation period was conducted for about 20 days from August 10, 2020. The final 291 copies were used for research analysis, and the SPSS 21.0 statistical package program was used for hypothesis verification. As a result of the analysis, the hygiene (��=.160), menu (��=.203), and packaging (��=.291) of Hypothesis 1 had a significant effect on reliability. Hypothesis 2's menu (��=.270), convenience (��=.201), and packaging (��=.195) were found to have a significant effect on differentiation. The reliability (��=.328) and differentiation (��=.443) of the brand image of Hypothesis 3 were found to have a significant effect on satisfaction (��=.428). Hygiene (��=.388), menu (��=.229), and convenience (��=.243) of Hypothesis 4 were analyzed to have a significant effect on satisfaction. Lastly, this study is expected to be provided as basic research data related to RMR products, and is intended to be presented as a theoretical basis for the use of marketing and direction in RMR product development of food service companies and restaurants.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.258-267
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• 2021

This study attempted an empirical analysis study on the Meal Kit Product, whose interest and demand continued to increase according to the eating out trend in the Untact era. In addition, this study attempted to investigate the relationship between the factors of Home Meal Replacement Meal Kit Product Selection Attributes, Consumers Satisfaction, and Other Recommendation Intention. Convenience sampling was used for consumers with experience in using Meal Kit Products released by food service companies and start-up companies. The investigation period was conducted for about one month from July 01, 2020, and the final 285 copies were used for analysis. The SPSS 21.0 statistical package program was used to verify the hypothesis. As a result of the analysis, the price (β=.241), convenience (β=.317), and diversity (β=.191) of Hypothesis 1 had a significant effect on Consumers Satisfaction. Price (β=.482), convenience (β=.133), and diversity (β=.342) were found to have a significant effect on the intention to recommend others. It was analyzed that Hypothesis 3's Consumers Satisfaction (β=.443) had a significant effect on Other Recommendation Intention. Finally, through this study, we expect to be provided as basic research data related to Meal Kit Product. It is intended to be presented as a theoretical basis for the use of marketing and direction for the development of milk kit products for catering companies and restaurants.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.1-17
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• 2012

Personalized smart devices such as smartphones and smart pads are widely used. Unlike traditional feature phones, theses smart devices allow users to choose a variety of functions, which support not only daily experiences but also business operations. Actually, there exist a huge number of applications accessible by smart device users in online and mobile application markets. Users can choose apps that fit their own tastes and needs, which is impossible for conventional phone users. With the increase in app demand, the tastes and needs of app users are becoming more diverse. To meet these requirements, numerous apps with diverse functions are being released on the market, which leads to fierce competition. Unlike offline markets, online markets have a limitation in that purchasing decisions should be made without experiencing the items. Therefore, online customers rely more on item-related information that can be seen on the item page in which online markets commonly provide details about each item. Customers can feel confident about the quality of an item through the online information and decide whether to purchase it. The same is true of online app markets. To win the sales competition against other apps that perform similar functions, app developers need to focus on writing app descriptions to attract the attention of customers. If we can measure the effect of app descriptions on sales without regard to the app's price and quality, app descriptions that facilitate the sale of apps can be identified. This study intends to provide such a quantitative result for app developers who want to promote the sales of their apps. For this purpose, we collected app details including the descriptions written in Korean from one of the largest app markets in Korea, and then extracted keywords from the descriptions. Next, the impact of the keywords on sales performance was measured through our econometric model. Through this analysis, we were able to analyze the impact of each keyword itself, apart from that of the design or quality. The keywords, comprised of the attribute and evaluation of each app, are extracted by a morpheme analyzer. Our model with the keywords as its input variables was established to analyze their impact on sales performance. A regression analysis was conducted for each category in which apps are included. This analysis was required because we found the keywords, which are emphasized in app descriptions, different category-by-category. The analysis conducted not only for free apps but also for paid apps showed which keywords have more impact on sales performance for each type of app. In the analysis of paid apps in the education category, keywords such as 'search+easy' and 'words+abundant' showed higher effectiveness. In the same category, free apps whose keywords emphasize the quality of apps showed higher sales performance. One interesting fact is that keywords describing not only the app but also the need for the app have asignificant impact. Language learning apps, regardless of whether they are sold free or paid, showed higher sales performance by including the keywords 'foreign language study+important'. This result shows that motivation for the purchase affected sales. While item reviews are widely researched in online markets, item descriptions are not very actively studied. In the case of the mobile app markets, newly introduced apps may not have many item reviews because of the low quantity sold. In such cases, item descriptions can be regarded more important when customers make a decision about purchasing items. This study is the first trial to quantitatively analyze the relationship between an item description and its impact on sales performance. The results show that our research framework successfully provides a list of the most effective sales key terms with the estimates of their effectiveness. Although this study is performed for a specified type of item (i.e., mobile apps), our model can be applied to almost all of the items traded in online markets.