• The Journal of Industrial Distribution & Business
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• v.11 no.2
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• pp.17-23
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• 2020

Purpose: The purpose of this study is to understand the effect of the unique product recommendation system on customer satisfaction. Research design, data and methodology: The survey method used the self-recording way in which the respondents selected for the study and distributed 300 questionnaires, and with due personal care, researchers collected all the distributed questionnaires. Results: The result implies that the characteristics of the product recommendation system should be more secure and developed. Conclusions: The aspects of the product recommendation system were selected as factors of price fairness, accuracy, and quality through previous studies, and the empirical analysis of the effect of the characteristics of the product recommendation system on customer satisfaction was summarized as follows. Among the attributes of the product recommendation system, the attributes of price fairness, accuracy, and quality affect customer satisfaction. Among them, the beta value of quality was the highest, and the effect of quality was the largest among the three factors. Based on the results of the study, the implications for the characteristics of the product recommendation system are summarized as follows. The aspects of the product recommendation system have a positive effect on customer satisfaction, so it is necessary to fill the needs of consumers based on the survey focused on quality

• Journal of Digital Contents Society
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• v.18 no.8
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• pp.1561-1566
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• 2017

Due to the advances in machine learning and artificial intelligence technologies, many new services have become available. Among such services, recommendation systems have already been successfully applied to commercial services and made profits as in online shopping malls. Most recommendation algorithms in commercial services are based on content analysis or explicit feedback rates as in movie recommendations. However, many online shopping malls have difficulties in content analysis or are lacking explicit feedbacks on their items, which results in no recommendation system for their items. Even for such service systems, user log data is easily available, and if recommendations are possible with such log data, the quality of their service can be improved. In this paper, we extract implicit feedback like click information for items from log data and provide a recommendation system based on the implicit feedback. The proposed system is applied to a real in-service online shopping mall.

• Journal of Information Technology Applications and Management
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• v.16 no.3
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• pp.17-28
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• 2009

This paper presents a novel method for evaluating the usefulness of online product recommendation. Previous studies on evaluating recommendation systems have mostly relied on two methods : testing the accuracy of estimating user preferences by recommendation systems, or empirically testing the effectiveness with lab experiments involving human participants. The former does not measure the usefulness directly and hence can be misleading; the latter is expensive in that it requires a working online store System and test participants. In order to address the problems, the proposed approach uses simulation to imitate customer behavior and evaluate the usefulness of recommendation. Models for user behavior and an abstract Internet store are developed for simulation. Actual simulation experiments are performed to illustrate the use of the approach.

• Journal of Distribution Science
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• v.16 no.8
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• pp.79-89
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• 2018

Purpose - This research aims to investigate the factors that influence consumer's overseas online shopping behavior. Consumers adopt overseas online shopping as a new buying way and more and more consumers prefer overseas online shopping than traditional shopping ways. Consumers' behaviors in this shopping experience can be different from other shopping experiences. With the increase of overseas online shopping, we need to find antecedents and results of overseas online shopping. Especially there would be positive or negative factors which influence overseas online shopping motivation. To find the relationship, this study examines self-efficacy and impulsivity as major factors which influence overseas online shopping. We also suggest that several attitude factors increase self-efficacy and it is positively related to customer satisfaction. On the other hand, we assume that overseas online shopping factors influence impulsivity of buying and it will decrease customer satisfaction. Research design, data, and methodology - This empirical study data were collected from Korean people who experience overseas online shopping. The subjects for this study were confined to shoppers who used overseas online shopping within the past six months. A total of 267 responses were gathered. SPSS 23.0, PLS 2.0 software were used in the data analysis. Descriptive statistics were used to show sample characteristics. We examined reliability, validity test for constructs. All measurement items used seven-point scales(1= very strong disagree, 7 = very strongly agree) drawn from previously published papers. Partial Least Square method was applied to find the relationship between antecedent factors and dependent factors and hypotheses were estimated. Results - Results show that perceived superiority, perceived ease of use, perceived transaction safety, perceived behavioral control positively affect self-efficacy. Self-efficacy influences positively to consumer's post purchase satisfaction. Perceived monetary benefit and perceived uniqueness motivated impulse buying. This can make consumer's post purchase dissatisfaction. Conclusions - This paper attempted to confirm the existence of both the positive and negative faces of overseas online shopping. The result reveals that self-efficacy is a major factor which may increase satisfaction in the overseas online shopping. Usually, we can think monetary benefit and uniqueness of products motivate overseas online shopping. But it can also intrigue impulse buying and negatively affect customer relationship. Therefore companies should provide enough products information to their potential customers and they might apply adequate processes such as recommendation, comparing systems to build long term relationship with their customers.

• Journal of Information Technology Applications and Management
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• v.24 no.4
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• pp.1-12
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• 2017

Item-based collaborative filtering (IBCF) is an important technology that is widely used in recommender system of online shopping malls. It uses historical information to compute item-item similarity and make predictions. However, in offline shopping each customer's purchasing pattern can be occurred continuously and repeatedly due to time and space constraints contrast to online shopping. Those facts can make IBCF to have limitations from being applied to offline shopping malls directly. In order to improve the quality of recommendations made by IBCF in offline shopping mall, we propose an ensemble approach that considers both item-item similarity of IBCF and each customer's purchasing patterns which are modeled by item networks. Our experimental results show that this approach produces recommendation results superior to those of existing works such as pure IBCF or bestseller approaches.

• Journal of Digital Convergence
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• v.18 no.4
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• pp.137-148
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• 2020

While the diversity of consumer choices due to the increased information in the digital age is positive, there are also many problems with the information overload. There are even situations in which consumers can not make the best choices under the weight of information. The purpose of this study is to look at how information overload plays a role in influencing online consumer behavior. With factors related to characteristics of the shopping mall, the recognition of the mall, the quality of the mall, the composition of the shopping mall, and the purchase recommendation service were set to analyze how these variables change the behavior of online consumers when information overload appears. According to the analysis results, all of characteristic factors of shopping malls set up in this paper are analyzed to have a constant effect on the behavior of online consumers, and information overload also has a constant medium effect on the recognition of shopping malls, the quality and the structure of shopping malls, and the provision of purchase recommendation services. And characteristic factors of shopping malls are also showing positive effects on online consumer behavior in information overload situations.

• 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.

• KIPS Transactions on Computer and Communication Systems
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• v.13 no.1
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• pp.10-20
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• 2024

As the online commerce market continues to expand with an increase of diverse products and content, users find it challenging in navigating and in the selection process. Thereafter both platforms and shopping malls are actively working in conducting continuous research on recommendations system to select and present products that align with user preferences. Most existing recommendation studies have relied on user data which is relatively easy to obtain. However, these studies only use a single type of event and their reliance on time dependent data results in issues with reliability and complexity. To address these challenges, this paper proposes a recommendation system that analysis user preferences in consideration of the relationship between various types of event data. The proposed recommendation system analyzes the correlation of multiple events, extracts weights, learns the recommendation model, and provides recommendation services through it. Through extensive experiments the performance of our system was compared with the previously studied algorithms. The results confirmed an improvement in both complexity and performance.

• Journal of the Korean Society of Clothing and Textiles
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• v.40 no.6
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• pp.1010-1024
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• 2016

This study identifies the influence of on/off-line acquaintances' recommendation messages on fashion product attitude and purchase intention on the online purchase of fashion products in two-sided word of mouth situations as well as compares the difference in influence according to bond-base with equidistance. This study was conducted for one month on university students in their 20s who were believed to be active in smartphone use. Out of the collected 174 copies of the questionnaire, 162 copies were used for analysis. The questionnaire was classified into online and offline recommendation messages of an acquaintance. We present two-sided fashion product reviews made similar to the type found in an actual shopping mall web-site. As for analysis, confirmatory factory analysis, structural equation modeling, and multi-group analysis were conducted using AMOS 19.0. The analysis results are as follows. First, on/off-line acquaintances' recommendation messages had significant influences on product attitude in the situation where two-sided reviews on fashion products were presented; however, those messages did not influence purchase intention. Recommendation messages positively increased product attitude and enhanced purchase intention if acquaintances' recommendation messages were mediated between on/off-line acquaintances' recommendation messages and purchase intention. Consequently, a mediating effect on product attitude was revealed. Second, there was no difference between online acquaintances and offline acquaintances in terms of the influence of acquaintances' recommendation messages on product attitude and purchase intention, in the situation where two-sided reviews were presented on online fashion products. Therefore, no control effect according to the type of acquaintance was confirmed.

• Fashion & Textile Research Journal
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• v.23 no.5
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• pp.586-597
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• 2021

Personalized product recommendation service is receiving attention as a new marketing strategy while supporting consumer information search and purchasing decisions. This study attempted to verify the effect of self-reference on service use behavior through the dual path of cognitive attitude and emotional attachment. Using convenience sampling, an online survey was conducted with 324 women who were in their 20s and 30s. After collecting and compiling the survey data, the reliability and validity of variables constituting the conceptual research model were verified through confirmatory factor analysis using AMOS 22.0. Next, the significance of sequentially mediated pathways was verified using Process 3.5 Model 80. The results showed that self-referencing not only significantly affects service use intention by simply mediating cognitive attitudes but also sequentially mediates cognitive attitudes and additional information search. Furthermore, self-referencing was significant as an indirect path to service use intention by mediating additional information search. However, in the path mediated by emotional attachment, self-referencing was considered as a simple mediated path leading to service usage intention. These results indicate a dual path in the psychological mechanism, through cognitive and emotional evaluation, that prompts consumer behavioral responses to the personalized product information provided in the shopping process.