• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.21 no.3
• /
• pp.99-105
• /
• 2021

In addition to the OTT video production service represented by Nexflix and YouTube, a personalized recommendation system for content with artificial intelligence has become common. YouTube's personalized recommendation service system consists of two neural networks, one neural network consisting of a recommendation candidate generation model and the other consisting of a ranking network. Netflix's video recommendation system consists of two data classification systems, divided into content-based filtering and collaborative filtering. As the online platform-led content production is activated by the Corona Pandemic, the field of virtual influencers using artificial intelligence is emerging. Virtual influencers are produced with GAN (Generative Adversarial Networks) artificial intelligence, and are unsupervised learning algorithms in which two opposing systems compete with each other. This study also researched the possibility of developing AI platform based on individual recommendation and virtual influencer (metabus) as a core content of OTT in the future.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.66 no.7
• /
• pp.1083-1091
• /
• 2017

As big data technologies have been developed and massive data have exploded from users through various channels, CEO of global IT enterprise mentioned core importance of data in next generation business. Therefore various machine learning technologies have been necessary to apply data driven services but especially recommendation has been core technique in viewpoint of directly providing summarized information or exact choice of items to users in information flooding environment. Recently evolved recommendation techniques have been proposed by many researchers and most of service companies with big data tried to apply refined recommendation method on their online business. For example, Amazon used item to item collaborative filtering method on its sales distribution platform. In this paper, we develop a commercial web service for suggesting music contents and implement three representative collaborative filtering methods on the service. We also produce recommendation lists with three methods based on real world sample data and evaluate the usefulness of them by comparison among the produced result. This study is meaningful in terms of suggesting the right direction and practicality when companies and developers want to develop web services by applying big data based recommendation techniques in practical environment.

• The Journal of the Korea Contents Association
• /
• v.22 no.7
• /
• pp.164-175
• /
• 2022

The OTT (Over The Top) platform promotes itself as a distinctive competitive advantage in that it allows users to stay on the platform longer and visit more often through a Personalized Recommendation Service. In this study, the characteristics of the Personalized Recommendation Service are divided into three categories: recommendation accuracy, recommendation diversity, and recommendation novelty. Then proposed a research model which affects the usefulness of users to recognize recommendation services by each characteristics and leads to satisfaction of expectations. The result of conducting an online survey of 300 people in their 20s and 30s who subscribe Netflix shows that the perceived usefulness increased when the accuracy, variety, and novelty of Netflix's Recommendation Service were high. It was also confirmed that high perceived usefulness leads to satisfaction of expectations before and after Netflix use. The derived research results can confirm the importance of evaluating the personalized recommendation service in terms of user experience and provide implications for ways to improve the quality of recommendation services.

• The Journal of the Korea Contents Association
• /
• v.18 no.12
• /
• pp.660-675
• /
• 2018

This study is designed to investigate how the perceived characteristics of the online video services affect the 'flow' as positive experience and satisfaction of users. For the study, we conducted a questionnaire survey on 289 people using YouTube, and then analyzed the relationships among variables using hierarchical regression analysis. As a result, it was confirmed that interactivity, newness of recommendation service, diversity of content, and entertainingness of contents all affect the lower level of flow experience. On the other hand, the accuracy of the recommendation service did not affect the flow experience, but positively affects the level of satisfaction. Finally, it is also confirmed that flow has a direct effect on user satisfaction, and mediates relationship between the characteristics of YouTube and satisfaction. The results of this study are helpful to understand user's perception and experience of online video platform service and suggest the discussion points to be considered by the industry to satisfy users.

• Information Systems Review
• /
• v.24 no.3
• /
• pp.69-91
• /
• 2022

The video recommendation services help to save the user's information search time in the overflowing online information, and algorithms for more efficient and accurate recommendation are continuously developed. In particular, TikTok has the largest number of users in the short video industry due to its unique recommendation algorithms. In this study, by applying a privacy calculation model, the research tried to compare users' responses to each type of TikTok's recommendation service. Users are well aware of the privacy concerns and benefits of TikTok's recommendation service. Although there is a risk, it was found that users continue to use TikTok's recommendation service because the benefits are greater.

• Information Systems Review
• /
• v.17 no.3
• /
• pp.95-111
• /
• 2015

The recommendation system automatically provides the predicted items which are expected to be purchased by analyzing the previous customer behaviors. This recommendation system has been applied to many e-commerce businesses, and it is generating positive effects on user convenience as well as the company's revenue. However, there are several limitations of the existing recommendation systems. They do not reflect specific criteria for evaluating products or the factors that affect customer buying decisions. Thus, our research proposes a collaborative recommendation model algorithm that utilizes each customer's online product reviews. This study deploys topic modeling method for customer opinion mining. Also, it adopts a kernel-based machine learning concept by selecting kernels explaining individual similarities in accordance with customers' purchase history and online reviews. Our study further applies a multiple kernel learning algorithm to integrate the kernelsinto a combined model for predicting the product ratings, and it verifies its validity with a data set (including purchased item, product rating, and online review) of BestBuy, an online consumer electronics store. This study theoretically implicates by suggesting a new method for the online recommendation system, i.e., a collaborative recommendation method using topic modeling and kernel-based learning.

• Journal of the Korean Society of Clothing and Textiles
• /
• v.43 no.3
• /
• pp.349-360
• /
• 2019

This study analyzes the trends of recommendation services for customized fashion styles in relation to artificial intelligence. To achieve this goal, the study examined filtering technologies of collaborative, content based, and deep-learning as well as analyzed the characteristics of recommendation services in the users' purchasing process. The results of this study showed that the most universal recommendation technology is collaborative filtering. Collaborative filtering was shown to allow intuitive searching of similar fashion styles in the cognition of need stage, and appeared to be useful in comparing prices but not suitable for innovative customers who pursue early trends. Second, content based filtering was shown to utilize body shape as a key personal profile item in order to reduce the possibility of failure when selecting sizes online, which has limits to being able to wear the product beforehand. Third, fashion style recommendations applied with deep-learning intervene with all user processes of buying products online that was also confirmed to penetrate into the creative area of image tag services, virtual reality services, clothes wearing fit evaluation services, and individually customized design services.

• International Journal of Computer Science & Network Security
• /
• v.22 no.6
• /
• pp.163-171
• /
• 2022

Online services are widely spread, and their use increases day by day. As COVID-19 spread and people spent much time online, fraud scams have risen unexpectedly. Manipulation techniques have become more effective at swindling those lacking basic technological knowledge. Unfortunately, a user needs a quorum. The interest in preventing scammers from obtaining effective quality service has become the most significant obstacle, increasing the variety of daily Internet platforms. This paper is concerned with analyzing purchase data and extracting provided results. In addition, after examining relevant documents presenting research discussing them, the recommendation was made that future work avoids them; this would save a lot of effort, money, and time. This research highlights many problems a person may face in dealing with online institutions and possible solutions to the epidemic through theft operations on the Internet.

• East Asian Journal of Business Economics (EAJBE)
• /
• v.7 no.1
• /
• pp.1-16
• /
• 2019

Purpose - For the development of fresh food shopping malls, consumers should continue to experience loyalty and favorability for the company's products or brands, and this should lead directly to purchase so that active word-ofmouth and recommendation should be encouraged. Therefore, the purpose of this study is to investigate the effect of e-service quality and e-ERM on e-loyalty with customer satisfaction and commitment as mediators. Research design, data, and methodology - This study was conducted by sample survey method on 320 online customers who have experience in using major online fresh food shopping malls for more than one year. Data analysis methods were frequency analysis, confirmatory factor analysis, reliability analysis, correlation analysis, and structural equation model analysis. Result - Hypothesis 1 through Hypothesis 7 were all supported. The results of this study suggest that e-service quality and e-CRM of online fresh food shopping malls have a significant effect on satisfaction and commitment. Therefore, the conclusion has been derived that the focus of this study, that such satisfaction and commitment have a significant effect on e-customer loyalty. has been supported theoretically and empirically. Conclusion - This study suggests that studies on customer loyalty based on activation commerce factors related to fresh food in online shopping malls will be an index that can reflect on customer's needs corresponding with future trends of not only online shopping malls but also offline shopping malls.

• Journal of Internet Computing and Services
• /
• v.21 no.1
• /
• pp.111-117
• /
• 2020

Dessert Ateliers (DA) are small shops that sell high-end homemade desserts such as macaroons, cakes, and cookies, and their popularity is increasing according to the emergence of small luxury trends. Even though each DA sells the same kinds of desserts, they are differentiated by the personality of their pastry chef; thus, there is a need to purchase desserts online that customers cannot see and purchase offline, and thus dessert e-commerce has emerged. However, it is impossible for customers to identify all the information of each DA and clearly understand customers' preferences when buying desserts through the dessert e-commerce. When a dessert e-commerce service provides a DA recommendation service, customers can reduce the time they hesitate before making a decision. Therefore, this paper proposes two kinds of DA recommendation method: a clustering-based recommendation method that calculates the similarity between customers' content and DAs and a dynamic weighting-based recommendation method that trains the importance of decision factors considering customer preferences. Various experiments were conducted using a real-world dataset to evaluate the performance of the proposed methods and it showed satisfactory results.