• Journal of Fashion Business
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• v.27 no.3
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• pp.50-62
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• 2023

The e-commerce fashion market has experienced a remarkable growth, leading to an overwhelming availability of shared information and numerous choices for users. In light of this, chatbots have emerged as a promising technological solution to enhance personalized services in this context. This study aimed to develop user-product attributes for a chatbot-based personalized fashion recommendation service using big data text mining techniques. To accomplish this, over one million consumer reviews from Coupang, an e-commerce platform, were collected and analyzed using frequency analyses to identify the upper-level attributes of users and products. Attribute terms were then assigned to each user-product attribute, including user body shape (body proportion, BMI), user needs (functional, expressive, aesthetic), user TPO (time, place, occasion), product design elements (fit, color, material, detail), product size (label, measurement), and product care (laundry, maintenance). The classification of user-product attributes was found to be applicable to the knowledge graph of the Conversational Path Reasoning model. A testing environment was established to evaluate the usefulness of attributes based on real e-commerce users and purchased product information. This study is significant in proposing a new research methodology in the field of Fashion Informatics for constructing the knowledge base of a chatbot based on text mining analysis. The proposed research methodology is expected to enhance fashion technology and improve personalized fashion recommendation service and user experience with a chatbot in the e-commerce market.

• Knowledge Management Research
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• v.21 no.3
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• pp.1-25
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• 2020

This study analyzed key success factors of Korean car-sharing enterprises, Socar and Greencar, and the responsive strategies of Korean car-manufacturing company, Hyundai Motor Group, in the face of emerging sharing economy under the specific economic and regulatory system in Korea. The outcomes of the analysis are as follows. 'Timely market entry' in early startup phase and 'use of external resources' in early growth phase were key success factors common to both Socar and Greencar. However, the differences in the eventual business directions of the two companies also resulted in different key success factors in the expansion phase of their business. For Socar which focused on maintaining its independence and the external growth of B2C business, customer relation marketing and sufficient capital raising were key success factors. For Greencar which became a part of a business group and focused on improving the efficiency of business operations, timely market entry (B2B market) was key success factor. The use of external resources and cooperation with large corporations emerged as key success factors common to both companies in the rapid growth phase. The responsive strategies of the Hyundai Motor Group were collaboration, investment and direct management of DeliveryCar. The short-term goal of the responsive strategy was the operation of test-bed in collaboration with car-sharing company while the mid/long term goal was planning new mobility services by utilizing collected data. Securing opportunities for early market dominance for autonomous car industry was also found to be an important goal.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.11
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• pp.5522-5529
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• 2013

The purpose of this study was to look into the marketing strategies of social commerce companies by analyzing the relationships among the environment, strategy and performance in social commerce. For that, the environment condition and components of marketing strategy were derived based on the marketing mix by analyzing the related contents published in 3,783 articles on newspapers dated from April 2010 to March 2013. UV(Unique Visitor) and PV(Page View) for each social commerce site were used as surrogates for performance. The results of study revealed the relationship of the marketing strategies to the changes in environment conditions towards negative conditions such as the spread of buyer anxiety. In the "strategy-performance" relations, the product element and external sales promotion element had high correlation with the performance. Finally, a difference was found in the marketing strategies of social commerce companies. High correlation was found in all aspects between the UV and PV marketing elements in the case of Coupang, while the correlation with the UV was low and the environment also showed relatively low correlation level in the case of WEmakePRICE. Thus, this study is considered to provide useful basis for the social commerce companies to map out and implement the marketing strategies, and is significant in that it applied the marketing mix to the special market environment such as social marketing.

• Journal of the Korea Convergence Society
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• v.12 no.8
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• pp.1-11
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• 2021

The purpose of this study was to analyze the trends and characteristics of 'stress urinary incontinence' research through word frequency analysis, and their relationships were modeled using word embedding. Abstract data of 9,868 papers containing abstracts in PubMed's MEDLINE were extracted using a Python program. Then, through frequency analysis, 10 keywords were selected according to the high frequency. The similarity of words related to keywords was analyzed by Word2Vec machine learning algorithm. The locations and distances of words were visualized using the t-SNE technique, and the groups were classified and analyzed. The number of studies related to stress urinary incontinence has increased rapidly since the 1980s. The keywords used most frequently in the abstract of the paper were 'woman', 'urethra', and 'surgery'. Through Word2Vec modeling, words such as 'female', 'urge', and 'symptom' were among the words that showed the highest relevance to the keywords in the study on stress urinary incontinence. In addition, through the t-SNE technique, keywords and related words could be classified into three groups focusing on symptoms, anatomical characteristics, and surgical interventions of stress urinary incontinence. This study is the first to examine trends in stress urinary incontinence-related studies using the keyword frequency analysis and word embedding of the abstract. The results of this study can be used as a basis for future researchers to select the subject and direction of the research field related to stress urinary incontinence.

• Smart Media Journal
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• v.13 no.5
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• pp.52-61
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• 2024

The onset of the COVID-19 pandemic has accelerated social transformations across various nations. These changes, particularly prominent in the corporate and industrial sectors, have necessitated a shift towards increased remote activities, fundamentally altering societal structures. Within this context, the concept of the Metaverse, a virtual world existing since the early 2000s but previously underrecognized, began to gain widespread recognition. In South Korea, major tech companies such as Naver, Kakao, and Coupang have long normalized remote working, with new employee orientations also taking place on Metaverse platforms. Beyond the IT sector, institutions requiring large gatherings, such as schools, have adopted the Metaverse for hosting major events like welcome ceremonies and informational sessions. This phenomenon suggests that the Metaverse is not merely a transient social trend but is gradually integrating into the daily lives of the general populace, serving as a significant social connector. This study explores the potential of Metaverse-enabled design thinking and methodologies to address the Environmental, Social, and Governance (ESG) challenges faced by Korean society. Specifically, the research focuses on developing solutions for social issues related to pets in Korea.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.