• Journal of Distribution Research
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• v.14 no.1
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• pp.27-44
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• 2009

This research classified the customer service factor of on-line shopping mall into tangibility, reliability, responsiveness, and empathy and analyzed the effect that the factors affect to consumer's purchase and re-purchase. If we present suggestions on the basis of these results of study, we would provide next two points: First, purchasers have utilized online shopping mall who pursued free from hard sell that being done in off-line and convenience of purchase affected more by reliability and responsiveness such as the fame of shopping mall that visit, reliability of security, and quick product search than the Customer of After-purchase Feedback influence for online purchasers decision factor out of consumer's purchase and re-purchase by on-line shopping mall customer service factor. Second, This study analyzed that online re-purchaser recognized the Customer of After-purchase Feedback factor high and built their loyalty through friendly emotion of on-line shopping mall and satisfaction of shopping mall service, and recommendation. In addition, they behave themselves as an affirmative messenger that is role of the Customer of After-purchase Feedback that make active opinion presentation and participation through community by important adjustment impact that empathy factor of on-line shopping mall customer service.

• Asia-Pacific Journal of Business
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• v.2 no.1
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• pp.91-114
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• 2011

The market size of e-Commerce in Japan was 15 trillion Yen in 2006, and B2C Internet shopping sales were over 6.57 trillion in 2009. Lakuten is a representative Internet shopping company whose market share is 45%. Lakuten has over 70,000 online stores and Japanese shoppers trust them based on the fair competition rule and pre-control system on e-commerce. Japanese consumers accept new technology rapidly and highly use Internet and mobile channel. This research analyse online shopping behaviors of Japan, a big e-commerce market. Internet shopping intention, satisfaction, and recommendation by Internet shopping motivations, perceived risks, shopping innovativeness were analyzed. A questionnaire survey of 464 Japanese consumer was performed and ANOVA, factor analysis, reliability test have done by SPSS 12.0. As the results, Internet shopping intentions were higher in groups of olders, higher innovativeness. House wives' satisfaction of Internet shopping is highest. High innovativeness group showed higher internet shopping motivation of economics, connivence, hedonic, and social. Student, women, and low income group perceives high risks to Internet shopping. Implications and further researches were suggested based on the results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.9
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• pp.1293-1304
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• 2022

The personalization recommendation system means analyzing each individual's interests or preferences and recommending information or products accordingly. These personalized recommendations can reduce the time consumers spend searching for information by accessing the products they need more quickly, and companies can increase corporate profits by recommending appropriate products that meet their needs. In this study, products are recommended to consumers using collaborative filtering, matrix factorization, and deep learning, which are representative personalization recommendation techniques. To this end, the data set after purchasing shopping mall products, which is raw data, is pre-processed in the form of transmitting the data set to the input of the recommended system, and the pre-processed data set is analyzed from various angles. In addition, each model performs verification and performance comparison on the recommended results, and explores the model with optimal performance, suggesting which model should be used when building the recommendation system at the mall.

• Journal of the Korea Convergence Society
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• v.9 no.12
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• pp.25-32
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• 2018

In the massive online fashion market, it is not easy for consumers to find the fashion style they want by keyword search for their preferred style. It can be resolved into consumer needs based fashion recommendation. Most of the existing online shopping sites have collected cumtomer's preference style using the online quastionnair. In this paper, we propose a simple but effective novel model that resolve the traditional method in fashion profiling for consumer's preference style and needs using implicit profiling method. In addition, we proposed a learning model that reflects the characteristics of the images itself through the deep learning-based intelligent preferred fashion model learned from the collected data. We show that the proposed model gave meaningful results through the qualitative evaluation.

• 한국경영정보학회:학술대회논문집
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• 2008.06a
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• pp.135-140
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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.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.5
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• pp.1-8
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• 2023

Cross-domain recommendation is a method that shares related user information data and item data in different domains. It is mainly used in online shopping malls with many users or multimedia service contents, such as YouTube or Netflix. Through K-means clustering, embeddings are created by performing clustering based on user data and ratings. After learning the result through a transformer network, user satisfaction is predicted. Then, items suitable for the user are recommended using a transformer-based recommendation model. Through this study, it was shown through experiments that recommendations can predict cold-start problems at a lesser time cost and increase user satisfaction.

• Asia pacific journal of information systems
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• v.12 no.3
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• pp.71-88
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• 2002

In this paper, intelligent marketing and merchandising methods utilizing data mining and Web mining techniques are proposed for online retailers to survive and succeed in gaining competitive advantage in a highly competitive environment. The first part of this paper explains the procedures of one-to-one marketing based on customer relationship management(CRM) techniques and personalized recommendation lists generation. The second part illustrates Web merchandising methods utilizing data mining techniques, such as association and sequential pattern mining. We expect that our Web marketing and merchandising methods will both provide a currently operating Internet shopping mall with more selling opportunities and give more useful product information to customers.

• Journal of the Korean Society for information Management
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• v.24 no.2
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• pp.89-104
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• 2007

This research attempts to give a personalized recommendation framework in large-sized music contents environment. Despite of existing studios and commercial contents for recommendation systems, large online shopping malls are still looking for a recommendation system that can serve personalized recommendation and handle large data in real-time. This research utilizes data mining technologies and new pattern matching algorithm. A clustering technique is used to get dynamic user segmentations using user preference to contents categories. Then a sequential pattern mining technique is used to extract contents access patterns in the user segmentations. And the recommendation is given by our recommendation algorithm using user contents preference history and contents access patterns of the segment. In the framework, preprocessing and data transformation and transition are implemented on DBMS. The proposed system is implemented to show that the framework is feasible. In the experiment using real-world large data, personalized recommendation is given in almost real-time and shows acceptable correctness.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.2
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• pp.176-182
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• 2023

With about 80% of the global economy expected to shift to the global market by 2030, exports of reverse direct purchase products, in which foreign consumers purchase products from online shopping malls in Korea, are growing 55% annually. As of 2021, sales of reverse direct purchases in South Korea increased 50.6% from the previous year, surpassing 40 million. In order for domestic SMEs(Small and medium sized enterprises) to enter overseas markets, it is important to come up with export strategies based on various market analysis information, but for domestic small and medium-sized sellers, entry barriers are high, such as lack of information on overseas markets and difficulty in selecting local preferred products and determining competitive sales prices. This study develops an AI-based product recommendation and sales price estimation model to collect and analyze global shopping malls and product trends to provide marketing information that presents promising and appropriate product sales prices to small and medium-sized sellers who have difficulty collecting global market information. The product recommendation model is based on the LTR (Learning To Rank) methodology. As a result of comparing performance with nDCG, the Pair-wise-based XGBoost-LambdaMART Model was measured to be excellent. The sales price estimation model uses a regression algorithm. According to the R-Squared value, the Light Gradient Boosting Machine performs best in this model.

• Journal of the Korean Society of Clothing and Textiles
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• v.45 no.1
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• pp.123-135
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• 2021

This study provides basic information for the convenient size selection of T-shirts in an online purchasing environment. The best preferred T-shirts fit was selected among five sizes of T-shirts according to body size group. The subjects were 103 students majoring in clothing. After setting a virtual model with her own body sizes, the subjects chose the best preferred fit among five sizes of T-shirts that included the one suitable to their bust circumference, two smaller T-shirts and two larger T-shirts. As a result, they preferred the fit of larger size T-shirts than body size, but they preferred a different fit by the body characteristic group such as waist height group and hip circumference group. T-shirt length was affected by waist height; in addition, shoulder ease was affected by hip circumference and bust circumference. Therefore waist height and hip circumference should be considerable sizes when consumers choose T-shirts sizes with a preferred fit.