• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.41-63
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• 2023

As the online e-commerce market growing, the need for a recommender system that can provide suitable products or services to customer is emerging. Recently, many studies using the sentiment score of online review have been proposed to improve the limitations of study on recommender systems that utilize only quantitative information. However, this methodology has limitation in extracting specific preference information related to customer within online reviews, making it difficult to improve recommendation performance. To address the limitation of previous studies, this study proposes a novel recommendation methodology that applies deep learning technique and uses various linguistic factors within online reviews to elaborately learn customer preferences. First, the interaction was learned nonlinearly using deep learning technique for the purpose to extract complex interactions between customer and product. And to effectively utilize online review, cognitive contents, affective contents, and linguistic style matching that have an important influence on customer's purchasing decisions among linguistic factors were used. To verify the proposed methodology, an experiment was conducted using online review data in Amazon.com, and the experimental results confirmed the superiority of the proposed model. This study contributed to the theoretical and methodological aspects of recommender system study by proposing a methodology that effectively utilizes characteristics of customer's preferences in online reviews.

• Information Systems Review
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• v.22 no.4
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• pp.75-92
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• 2020

As population and generation structures change, more and more customers tend to avoid facing relation due to the development of information technology and spread of smart phones. This phenomenon consists with efficiency and immediacy, which are the consumption patterns of modern customers who are used to information technology, so offline network-oriented distribution companies actively try to switch their sales and services to untact patterns. Recently, untact services are boosted in various fields, but beauty products are not easy to be recommended through untact services due to many options depending on skin types and conditions. There have been many studies on recommendations and development of recommendation systems in the online beauty field, but most of them are the ones that develop recommendation algorithm using survey or social data. In other words, there were not enough studies that classify segments based on user information such as skin types and product preference. Therefore, this study classifies customer segments using machine learning technique K-prototypesalgorithm based on customer information and search log data of mobile application, which is one of untact services in the beauty field, based on which, untact marketing strategy is suggested. This study expands the scope of the previous literature by classifying customer segments using the machine learning technique. This study is practically meaningful in that it classifies customer segments by reflecting new consumption trend of untact service, and based on this, it suggests a specific plan that can be used in untact services of the beauty field.

• Culinary science and hospitality research
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• v.12 no.3 s.30
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• pp.1-16
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• 2006

This study investigates the level of customers' preferences for the menu at rice cake cafes. The study aims at examining the potential of restaurants with rice cake dishes and suggesting the ways to contribute to the potential. For this study, various rice cake cafes in Seoul which were often mentioned in the Internet portal sites and mass media are selected such as Jilsilu, Midan, Yaemunbyeonggwa, and Howeondang. Some of the customers in these rice cake cafes from August 16th to 26th in 2005 are the participants in this study. Using Windows SPSS 11.0 for statistical analysis, the validity of the items in the questionnaire is tested through Cronbach's Alpha which also reveals the internal reliability. Variables are analysed to eliminate less important variables. In order to examine the differences between groups by norm population profiles, t-test and ANOVA are conducted. For obtaining the degree of impact of rice cake cafe preference variables on the satisfaction in the rice cake cafe, regression analysis is carried out. As a result, for preferences for dishes, 19 variables among 22 are identified and divided into four factors which are named as 1) fundamental factor, 2) external factor, 3) appearance factor, and 4) structural factor. The fundamental factor, external factor, appearance factor, and structural factor are all significantly influential to the level of satisfaction with the menu, yet the fundamental factor is the most powerful factor among them.

• Culinary science and hospitality research
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• v.21 no.4
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• pp.86-100
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• 2015

The purpose of this study is to investigate how the food lifestyle of customers affects MSG usage at restaurants and to identify the moderating effects of customer attitude to food safety using a hierarchical regression analysis suggested by Baron & Kenny. The findings and implications can be summarized as follows. First, customers are classified into five groups: food-explore group, taste-oriented group, health-oriented, convenience-oriented group, and tradition-oriented group. Second, the convenience-oriented group has significant positive effects, and the health-oriented group and tradition-oriented group have significant negative effects on preference for MSG use at restaurants. Third, there is significant negative moderating effect of the convenience-oriented group and the tradition-oriented group between their food lifestyle and preference for MSG at restaurants.

• The KIPS Transactions:PartC
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• v.13C no.4 s.107
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• pp.415-426
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• 2006

The essential characteristic of ubiquitous is context-awareness, and that means ubiquitous computing can automatically process the data that change according to space and time, without users' intervention. However, in circumstance of context awareness, since location information is able to be collected without users' clear approval, users cannot control their location information completely. These problems can cause privacy issue when users access their location information. Therefore, it is important to construct the location information system, which decides to release the information considering privacy under the condition such as location, users' situation, and people who demand information. Therefore, in order to intercept an outflow information and provide securely location-based information, this paper suggests a new system based CS-RBAC with the existing LBS, which responds sensitively as customer's situation. Moreover, it accommodates a merit of PCP reflecting user's preference constructively. Also, through privacy weight, it makes information not only decide to providing information, but endow 'grade'. By this method, users' data can be protected safely with foundation of 'Role' in context-aware circumstance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.2
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• pp.228-233
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• 2021

As the number of successful design innovation product cases in business increases, interest in design innovation is increasing. This study aims to examine how the design innovation (functionality, ergonomics, aesthetics) of Bluetooth speaker products affects the customer's preference and purchase intention. It proposes a research model through rational behavior theory (TRA) and technology acceptance model (TAM) for empirical research. The questionnaire was composed of questions to understand the influence of design innovation, favorability, and purchase intention. As a result of the study, functionality, ergonomics, and aesthetics influenced product preference and purchase intention. In order for the innovative product of Bluetooth speaker design to be accepted in the early market, it is most important to form a positive attitude toward favorability centering on function and aesthetics. Favorability is a factor that has the most decisive influence on the purchase intention of design innovation products, and companies must discover and reinforce various factors that positively affect the preference.

• Journal of Digital Convergence
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• v.10 no.5
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• pp.67-78
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• 2012

Recently, the interesting of customers in online auto insurance is rapidly increasing. The one of major reasons is economical benefit. offline auto insurance products as a service formed high price. However, online auto insurance relatively formed low price. Thanks to these characteristics of online auto insurance has gained great popularity. Therefore, in purchasing online auto insurance, consumers carefully buy products of auto insurance. In this study, we classified the $2{\times}2$ matrix (online preference group, economic pursuit group, convenience oriented group, and carefulness approach group) in online auto insurance consumers focusing on the perceived benefits and price acceptance. From an economic point of view of consumers around the perceived benefits and price acceptance, we analyzed the relationships among easy of use, usefulness, attitude, and purchase intention in automobile e-shopping mall. The results of this study will provide the useful implications for the planing CRM(customer relationship management) strategy for improving purchase intention of customers to online insurance companies.

• Journal of the Ergonomics Society of Korea
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• v.29 no.4
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• pp.631-637
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• 2010

The aim of affective engineering is to develop a new product by translating customer affections into design factors. Affective data have so far been analyzed using a multivariate statistical analysis, but the affective data do not always have linear features assumed under normal distribution. Rough sets model is an effective method for knowledge discovery under uncertainty, imprecision and fuzziness. Rough sets model is to deal with any type of data regardless of their linearity characteristics. Therefore, this study utilizes rough sets model to extract affective knowledge from affective data. Four types of scent alternatives and four types of sounds were designed and the experiment was performed to look into affective differences in subject's preference on air conditioner. Finally, the purpose of this study also is to extract knowledge from affective data using rough sets model and to figure out the relationships between rough sets based affective engineering method and statistical one. The result of a case study shows that the proposed approach can effectively extract affective knowledge from affective data and is able to discover the relationships between customer affections and design factors. This study also shows similar results between rough sets model and statistical method, but it can be made more valuable by comparing fuzzy theory, neural network and multivariate statistical methods.

• Journal of the Korean Dietetic Association
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• v.24 no.3
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• pp.246-260
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• 2018

This study examined the customers' perception on Home Meal Replacement (HMR) products. Specifically, there were three research objectives: 1. to identify the customers' HMR purchase patterns and preference of HMR product development; 2. to identify the attributes of the HMR products that the customers perceive; and 3. to examine the customers' level of expectation-confirmation toward HMR product attributes according to the demographic characteristics. This study employed a self-administered survey that was distributed online from November 21~24, 2017. The sample of the study was the customers who had purchased HMR products in the six months prior to taking the survey. A total of 553 respondents completed the survey, which was used for data analysis. The results revealed the customers' HMR purchase patterns. The major HMR product type of purchase was ready to heat (52.6%), while the main reason for purchasing HMR products was convenience (83.2%). For the differences in the level of expectation-confirmation toward HMR products in accordance with the demographic characteristics of customers, the results indicated that there was a difference in the expectation-confirmation level according to age, whereas the respondents aged 29 and under showed a significantly higher level of time-saving for the preparation and ease of cooking (P<0.05) than the other age groups. In addition, there was a significant difference in the expectation-confirmation level for saving meal preparation time (P<0.05) and convenience (P<0.01) among the customer's occupation. These findings can provide the basis for a strategy for developing HMR products reflecting the rapidly changing customers' needs. HMR products should be developed according to the specific target market, as the study indicated that the respective customer segmentation resulted in a difference in their expectation toward HMR products.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.129-142
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• 2016

Customer product reviews have become one of the important factors for purchase decision makings. Customers believe that reviews written by others who have already had an experience with the product offer more reliable information than that provided by sellers. However, there are too many products and reviews, the advantage of e-commerce can be overwhelmed by increasing search costs. Reading all of the reviews to find out the pros and cons of a certain product can be exhausting. To help users find the most useful information about products without much difficulty, e-commerce companies try to provide various ways for customers to write and rate product reviews. To assist potential customers, online stores have devised various ways to provide useful customer reviews. Different methods have been developed to classify and recommend useful reviews to customers, primarily using feedback provided by customers about the helpfulness of reviews. Most shopping websites provide customer reviews and offer the following information: the average preference of a product, the number of customers who have participated in preference voting, and preference distribution. Most information on the helpfulness of product reviews is collected through a voting system. Amazon.com asks customers whether a review on a certain product is helpful, and it places the most helpful favorable and the most helpful critical review at the top of the list of product reviews. Some companies also predict the usefulness of a review based on certain attributes including length, author(s), and the words used, publishing only reviews that are likely to be useful. Text mining approaches have been used for classifying useful reviews in advance. To apply a text mining approach based on all reviews for a product, we need to build a term-document matrix. We have to extract all words from reviews and build a matrix with the number of occurrences of a term in a review. Since there are many reviews, the size of term-document matrix is so large. It caused difficulties to apply text mining algorithms with the large term-document matrix. Thus, researchers need to delete some terms in terms of sparsity since sparse words have little effects on classifications or predictions. The purpose of this study is to suggest a better way of building term-document matrix by deleting useless terms for review classification. In this study, we propose neutrality index to select words to be deleted. Many words still appear in both classifications - useful and not useful - and these words have little or negative effects on classification performances. Thus, we defined these words as neutral terms and deleted neutral terms which are appeared in both classifications similarly. After deleting sparse words, we selected words to be deleted in terms of neutrality. We tested our approach with Amazon.com's review data from five different product categories: Cellphones & Accessories, Movies & TV program, Automotive, CDs & Vinyl, Clothing, Shoes & Jewelry. We used reviews which got greater than four votes by users and 60% of the ratio of useful votes among total votes is the threshold to classify useful and not-useful reviews. We randomly selected 1,500 useful reviews and 1,500 not-useful reviews for each product category. And then we applied Information Gain and Support Vector Machine algorithms to classify the reviews and compared the classification performances in terms of precision, recall, and F-measure. Though the performances vary according to product categories and data sets, deleting terms with sparsity and neutrality showed the best performances in terms of F-measure for the two classification algorithms. However, deleting terms with sparsity only showed the best performances in terms of Recall for Information Gain and using all terms showed the best performances in terms of precision for SVM. Thus, it needs to be careful for selecting term deleting methods and classification algorithms based on data sets.