• The Journal of the Korea Contents Association
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• v.19 no.3
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• pp.171-182
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• 2019

As online shopping malls continue to grow in popularity, various chances of consumption are provided to customers. Customers decide the purchase by exploiting information provided by shopping malls such as the reviews of actual purchasing users, the detailed information of items, and so on. It is required to provide objective and reliable information because customers have to decide on their own whether the massive information is credible. In this paper, we propose a personalized recommendation method considering an item confidence to recommend reliable items. The proposed method determines user preferences based on various behaviors for personalized recommendation. We also propose an user preference measurement that considers time weights to apply the latest propensity to consume. Finally, we predict the preference score of items that have not been used or purchased before, and we recommend items that have highest scores in terms of both the predicted preference score and the item confidence score.

• Journal of the Korean Society of Costume
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• v.64 no.8
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• pp.1-19
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• 2014

The purpose of this study is to do an in-depth examination of Korean women in their 30's on the characteristics of their online fashion shopping, clothes-wearing, and presentation. In-depth interview and quantitative analysis were conducted as study methods. Results of this in-depth analysis showed that the factor with the most significant influence in their lifestyle was "childbirth." Childbirth was a major factor during fashion shopping and clothes-wearing. Also the results showed that the reason they used online shopping was for convenience, efficiency, rationality, pursuit of information, variety, and hedonism. In particular, women in their 30's had a higher motivation for efficiency and rationality compared to those in their 20's, and of those women, married working women showed the highest preference for fashion soho malls. Meanwhile, full-time homemakers, who pursued rationality, used open markets to search for fashion items based on price. Furthermore, the factors that women in their 30's considered during online shopping were price, design, purpose or situation for wearing the clothing, respectively. Compared to the women in their 20's, they emphasized recommendation, product properties, credibility, economy more than women in their 20's. Factors such as marriage and childbirth were more influential than occupation. Meanwhile, the factors that women in their 30's considered for wearing and presentation were time, place, and occasion(TPO), which all showed high importance in in-depth interview and quantitative analysis. Other factors were 'suitable image to self' and 'covering up body figure.'

• Journal of Fashion Business
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• v.25 no.5
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• pp.25-37
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• 2021

As the online shopping market grows, channels in the mobile shopping environment have become increasingly diverse as a wide variety of products are introduced every day. This study investigated the effects of the self-construal type, mobile product recommendation system type, and fashion product type on purchase intention. The experimental design of this study was a 2 (self-construal type: independent vs. interdependent) × 2 (product recommendation system: bestseller vs. content-based) × 2 (fashion product type: utilitarian vs. hedonic) 3-way mixed ANOVA. Women (n = 387) in their 20 to 30s residing in Seoul and the Gyeonggi area participated in the study. The data were analyzed with the SPSS 24 program and 3-way ANOVA and simple main effects analyses were conducted. The results were as follows. First, self-construal, product recommendation, and fashion product types had a statistically significant impact on purchase intention. Second, fashion product and consumers' self-construal types had significant interaction effects on purchase intention. Finally, product recommendation and fashion product and self-construal types showed significant 3-way interaction effects on purchase intention. The study confirmed an interaction between the self-construal, type of product recommendation system, and the type of fashion product used in influencing purchase intention.

• Journal of the Korean Society of Clothing and Textiles
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• v.42 no.5
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• pp.871-883
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• 2018

This study tests consumer responses to online product recommendation service offered by a website. A product recommendation service refers to a filtering system that predicts and shows items that consumers would like to purchase based on their searches or pre-purchase information. The survey is conducted on 300 people in an age group between 20 and 40 years in a panel of an online survey firm. Data are analyzed using confirmatory factor analysis and structural equation modeling by AMOS 20.0. The results show that personalization quality does not have a significant effect on trust, but relationship quality and technology quality have a positive effect on trust. Three types of quality of recommendation service also have a positive effect on commitment. Trust and commitment are factors that increase service usage intentions. In addition, this study reveals the moderating effect of light users vs heavy users based on online shopping time. Light users show a negative effect of personalization quality on trust, indicating that they are likely to be uncomfortable to the service using personal information, compared to heavy users. This study also finds that trust vs commitment is an important factor increasing service usage intentions for heavy users vs light users.

• Journal of the Korean Operations Research and Management Science Society
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• v.33 no.3
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• pp.59-76
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• 2008

Studies of recommender systems have focused on improving their performance in terms of error rates between the actual and predicted preference values. Also, many studies have been conducted to investigate the relationships between customer information processing and the characteristics of recommender systems via surveys and web-based experiments. However, the actual impact of recommendation on product pages for customer browsing behavior and decision-making in the commercial environment has not, to the best of our knowledge, been investigated with actual clickstream data. The principal objective of this research is to assess the effects of product recommendation on customer behavior in e-Commerce, using actual clickstream data. For this purpose, we utilized an online bookstore's clickstream data prior to and after the web site renovation of the store. We compared the recommendation effects on customer behavior with the data. From these comparisons, we determined that the relevant recommendations in product pages have positive relationships with the acquisition of customer attention and elaboration. Additionally, the placing of recommended items in shopping cart is positively related to suggesting the relevant recommendations. However, the frequencies at which the recommended items were purchased did not differ prior to and after the renovation of the site.

• Journal of the Korea Fashion and Costume Design Association
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• v.14 no.3
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• pp.161-176
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• 2012

This study aims to find out the influence of satisfaction of the product and shopping mall attributes on mall reliability, repurchase intention, and recommendation intention in internet open market. For this purpose, this study surveyed 266 male and female consumers in their 20's~40's for empirical analysis who have ever purchased clothing through internet open markets. Respondents are selected using the convenience sampling through online survey in August 2011. For statistical analysis, descriptive statistics, reliability analysis, factor analysis, t-test, ANOVA, and regression analysis are carried out using SPSS for Windows 12.0. The results are as follows; First, it was identified that there were Significant differences in consumers' satisfaction on product and shopping mall attributes according to purchase price, degree of purchase, and the demographics. Second, it was identified that performance, sewing condition, the stability of the form, texture, harmony with other clothes, the response of people, fashionability, seller, origin, detailed explanation on products, interaction with shopping malls, and ease-of-use have significant influence on the reliability of open market. Third, it was identified that easiness to be active in, the stability of the food, design, suitability to T.P.O, price, origin, detailed explanation on products, product assortment, reputation of shopping malls, ease-of-use, and delivery charge policy have significant influence on the repurchase intention. Fourth, it was identified that easiness to be active in, the stability of the form, design, suitability to T.P.O, price, origin, detailed explanation on products, product assortment, reputation of shopping malls, ease-of-use, and delivery charge policy have significant influence on the intention to recommend.

• The Journal of Society for e-Business Studies
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• v.16 no.3
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• pp.191-209
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• 2011

In a buyer-seller transaction process, 'value for money,' a measure of quality-price-ratio, is one of the most important criteria for buyers' purchasing decisions. The purpose of this paper is to suggest a method which helps online shoppers choose the best of several sellers offering homogeneous goods. We suggest FDH (free disposal hull) model, an applied model of data envelopment analysis (DEA), for online buyer-seller transactions and verify it with the data from an Internet comparison shopping site. For this purpose, we analyze consumer choice behaviors by examining how consumers respond to different sale conditions such as price, brand, or delivery time. Then, we implement a seller recommendation system to support buyers' purchasing decisions. We expect our FDH model to provide valuable information for rational buyers who want to pay the least price for high quality products/services and to be used in implementing automated evaluation processes in micro transactions. Moreover, we expect that our results can be utilized for sellers' benchmarking strategies which help sellers be more competitive by showing them how to attract buyers.

• Journal of Korea Multimedia Society
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• v.12 no.5
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• pp.688-698
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• 2009

The market for Internet fashion/coordination shopping malls has been enormously increased year by year. However, online shoppers feel inconvenient because most of Internet shopping malls still rely on item classifications by category and do not provide the functionality in terms of which shoppers can find clothes they want. In an effort to build a fashion/coordination system for women's dress adopting the Heuristic-based method, one of the Context-based methods, we present a method for defining characteristics of a woman's dress as attributes and their inheritance relations, which can be input by a product manager. We also compare and analyze various methods for recommending the most similar clothes.

• CRM연구
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• v.4 no.2
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• pp.19-29
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• 2011

There is a large difference between purchasing patterns in an online shopping mall and in an offline market. This difference may be caused mainly by the difference in accessibility of online and offline markets. It means that an interval between the initial purchasing decision and its realization appears to be relatively short in an online shopping mall, because a customer can make an order immediately. Because of the short interval between a purchasing decision and its realization, an online shopping mall transaction usually contains fewer items than that of an offline market. In an offline market, customers usually keep some items in mind and buy them all at once a few days after deciding to buy them, instead of buying each item individually and immediately. On the contrary, more than 70% of online shopping mall transactions contain only one item. This statistic implies that traditional data mining techniques cannot be directly applied to online market analysis, because hardly any association rules can survive with an acceptable level of Support because of too many Null Transactions. Most market basket analyses on online shopping mall transactions, therefore, have been performed by expanding the co-occurrence criteria of traditional association rule mining. While the traditional co-occurrence criteria defines items purchased in one transaction as concurrently purchased items, the expanded co-occurrence criteria regards items purchased by a customer during some predefined period (e.g., a day) as concurrently purchased items. In studies using expanded co-occurrence criteria, however, the criteria has been defined arbitrarily by researchers without any theoretical grounds or agreement. The lack of clear grounds of adopting a certain co-occurrence criteria degrades the reliability of the analytical results. Moreover, it is hard to derive new meaningful findings by combining the outcomes of previous individual studies. In this paper, we attempt to compare expanded co-occurrence criteria and propose a guideline for selecting an appropriate one. First of all, we compare the accuracy of association rules discovered according to various co-occurrence criteria. By doing this experiment we expect that we can provide a guideline for selecting appropriate co-occurrence criteria that corresponds to the purpose of the analysis. Additionally, we will perform similar experiments with several groups of customers that are segmented by each customer's average duration between orders. By this experiment, we attempt to discover the relationship between the optimal co-occurrence criteria and the customer's average duration between orders. Finally, by a series of experiments, we expect that we can provide basic guidelines for developing customized recommendation systems.

• Journal of Intelligence and Information Systems
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• v.11 no.1
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• pp.191-205
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• 2005

This paper presents a novel product recommender system as a tool fur differentiated marketing service of online shopping malls. Ihe proposed model uses genetic algorithnt one of popular global optimization techniques, to construct a personalized product recommender systen The genetic algorinun may be useful to recommendation engine in product recommender system because it produces optimal or near-optimal recommendation rules using the customer profile and transaction data. In this study, we develop a prototype of WeLbased personalized product recommender system using the recommendation rules fi:om the genetic algorithnL In addition, this study evaluates usefulness of the proposed model through the test fur user satisfaction in real world.