• Information Systems Review
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• v.9 no.2
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• pp.109-127
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• 2007

In a buyer seller transaction process, "value for money" is one of the most important criteria for a buyer's purchasing decision. The terms "value" and "money" represent a composite measure of what a buyer receives from goods and/or services and a measure of what he/she pays for them, respectively. The purpose of this paper is to help buyers select the best seller in terms of value for money. We suggest DEA models for buyer seller transactions and apply them to the case of an Internet comparison shopping site in Korea. We expect our DEA models to provide valuable information for rational buyers who want to pay the least price for high quality products/services. Moreover, we expect that our models can help sellers be more competitive by showing them how to attract buyers.

• Information Systems Review
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• v.18 no.2
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• pp.59-77
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• 2016

Although various recommendation techniques have been applied to the e-commerce market, few studies compare the intent to use these techniques from the customer's perspective. In this paper, we conduct a comparative analysis of customers' intention to use five recommendation techniques widely adapted by online shopping malls and focus on the differences in purchasing electronic goods and apparel products. The recommendation techniques are as follows: best-seller recommendation, merchandiser recommendation, content-based recommendation, collaborative filtering recommendation, and social recommendation. Additionally, we examine which factors influence customer intent to use the recommendation services. Data were collected through a survey administered to 220 e-commerce users with prior experience with recommendation services. Collected data were examined using analysis of variance and regression analysis. Results indicate statistically significant differences in customers' intention to use recommendation services according to the recommendation technique. In particular, the best-seller recommendation technique is preferred when purchasing electronic goods, whereas the content-based recommendation technique is preferred for apparel purchases. Factors such as personal characteristics and personality, purchasing tendency, as well as perception of the product or recommendation service affect a customer's intention to use a recommendation service. However, the influence of these factors varies depending on the recommendation technique. This study provides guidelines for companies to adopt appropriate recommendation techniques according to product categories and personal characteristics of customers.

• 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 Intelligence and Information Systems
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• v.15 no.1
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• pp.123-140
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• 2009

Collaborative filtering recommends products using customers' preferences, so it cannot recommend products to the new customer who has no preference information. This paper proposes a novel approach to new customer recommendations using the social network analysis which is used to search relationships among social entities such as genetics network, traffic network, organization network, etc. The proposed recommendation method identifies customers most likely to be neighbors to the new customer using the centrality theory in social network analysis and recommends products those customers have liked in the past. The procedure of our method is divided into four phases : purchase similarity analysis, social network construction, centrality-based neighborhood formation, and recommendation generation. To evaluate the effectiveness of our approach, we have conducted several experiments using a data set from a department store in Korea. Our method was compared with the best-seller-based method that uses the best-seller list to generate recommendations for the new customer. The experimental results show that our approach significantly outperforms the best-seller-based method as measured by F1-measure.

• IE interfaces
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• v.15 no.2
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• pp.107-113
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• 2002

Recent reports studied that injuries or deaths frequently occurred in consumer product accidents by product defects. Broadly speaking, product liability is liability which is imposed upon a manufacturer or other seller for personal injury, death, property damage and/or commercial loss arising with respect to a product or service provided by it. In this study, we want to search a method of prevention against appling PL laws. The way was researching on the level of appreciation of PL law, warning messages's means and design criteria for seller or consumer of child toys. As a result, most people didn't understand PL laws. Although they read them before purchasing child toy, many consumers didn't differentiate means of "Notice", "Warning", and "Danger" in warning messages. In addition, they considered important factors in warning messages as notice warning, safety mark(UL, etc), age recommendation and color in order. This study will be effective to search a method of prevention against PL laws.

• Asia pacific journal of information systems
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• v.16 no.4
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• pp.239-254
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• 2006

Location-based advertising or application has been one of the drivers of third-generation mobile operators' marketing efforts in the past few years. As a result, many studies on location-based marketing or advertising have been proposed for recent several years. However, these approaches have two common shortcomings. First. most of them just suggested the theoretical architectures, which were too abstract to apply it to the real-world cases. Second, many of these approaches only consider service provider (seller) rather than customers (buyers). Thus, the prior approaches fit to the automated sales or advertising rather than the implementation of CRM. To mitigate these limitations, this study presents a novel advertisement recommendation model for mobile users. We call our model MAR-CF (Mobile Advertisement Recommender using Collaborative Filtering). Our proposed model is based on traditional CF algorithm, but we adopt the multi-dimensional personalization model to conventional CF for enabling location-based advertising for mobile users. Thus, MAR-CF is designed to make recommendation results for mobile users by considering location, time, and needs type. To validate the usefulness of our recommendation model. we collect the real-world data for mobile advertisements, and perform an empirical validation. Experimental results show that MAR-CF generates more accurate prediction results than other comparative models.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.99-114
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• 2011

Collaborative Filtering (CF) suffers from two major problems:sparsity and cold-start recommendation. This paper focuses on the cold-start problem for new customers with no purchase records and the sparsity problem for the customers with very few purchase records. For the purpose, we propose a method for the new customer recommendation by using a combined measure based on three well-used centrality measures to identify the customers who are most likely to become neighbors of the new customer. To alleviate the sparsity problem, we also propose a hybrid approach that applies our method to customers with very few purchase records and CF to the other customers with sufficient purchases. To evaluate the effectiveness of our method, we have conducted several experiments using a data set from a department store in Korea. The experiment results show that the combination of two measures makes better recommendations than not only a single measure but also the best-seller-based method and that the performance is improved when applying the hybrid approach.

• Journal of Internet Computing and Services
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• v.20 no.1
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• pp.57-65
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• 2019

In the case of Domeggook B2B online shopping malls, it has a market share of over 70% with more than 2 million members and 800,000 items are sold per one day. However, since the same or similar items are stored and registered in different catalog entries, it is difficult for the buyer to search for items, and problems are also encountered in managing B2B large shopping malls. Therefore, in this study, we developed a catalog entry auto classification and recommendation system for products by using semi-supervised machine learning method based on previous huge shopping mall purchase information. Specifically, when the seller enters the item registration information in the form of natural language, KoNLPy morphological analysis process is performed, and the Naïve Bayes classification method is applied to implement a system that automatically recommends the most suitable catalog information for the article. As a result, it was possible to improve both the search speed and total sales of shopping mall by building accuracy in catalog entry efficiently.

• Smart Media Journal
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• v.10 no.1
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• pp.116-123
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• 2021

Due to the rapid growth of the online fashion market and the resulting expansion of online choices, there is a problem that the seller cannot directly respond to a large number of consumers individually, although consumers are increasingly demanding for more personalized recommendation services. Images are being tagged as a way to meet consumer's personalization needs, but when people tagging, tagging is very subjective for each person, and artificial intelligence tagging has very limited words and does not meet the needs of users. To solve this problem, we designed an algorithm that recognizes the shape, attribute, and emotional information of the product included in the image with AI, and codes this information to represent all the information that the image has with a combination of codes. Through this algorithm, it became possible by acquiring a variety of information possessed by the image in real time, such as the sensibility of the fashion image and the TPO information expressed by the fashion image, which was not possible until now. Based on this information, it is possible to go beyond the stage of analyzing the tastes of consumers and make hyper-personalized clothing recommendations that combine the tastes of consumers with information about trends and TPOs.

• 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.