• 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.

• 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.

• Asia Marketing Journal
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• v.10 no.2
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• pp.125-151
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• 2008

Both companies and consumers are highly interested in on-line customer reviews which enable consumers to share their experience and knowledge about products. In this study, after classifying real reviews into context units and deriving categories, we analyzed differences between categories based on channel(manufacturers' homepage/ shopping mall), product attribute(search/experience) and price(high/low). The method to derive categories is based on roughly adopting constructs of ACSI model and elaborate and repetitive classification of real reviews. We set up the classification category with 3 levels. Level 1 consists of product and service, level 2 consists of function, design, price, purchase motive, suggestion/user-tip and recommendation/repurchase in product and AS/up-grade and delivery/others in service and level 3 is composed of details of level 2 of category. We could find remarkable differences between channels in all 8 items of level 2 of category. As the number of context units in homepage is more than in shopping mall, we found reviews in homepage is more concrete. Moreover, overall satisfaction in review was higher at homepage's. Also, in product attribute dimension, we found different patterns of reviews in design, purchase motive, suggestion/user-tip, recommendation/repurchase, AS/up-grade and delivery/others and no difference in overall customer's satisfaction. In price dimension, we found differences between high and low price in design, price and AS/up-grade and no difference in overall customer's satisfaction.

• The Journal of the Convergence on Culture Technology
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• v.8 no.3
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• pp.435-439
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• 2022

Many of the industry was changed because of the pandemic of covid 19. It combined with the tendency of modern people to pursue convenience. The industry of Cosmetics also changed business channel from offline to online. Before, people can not get suggestions after they complete the survey. This paper research how to suggest some cosmetics products with their skin type and skin data. We will develop Beauty Concierge system that can get suggestion after the survey. It's will make people attend activity and can make more benefit to the people.

• Journal of Korean Library and Information Science Society
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• v.53 no.1
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• pp.231-263
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• 2022

The understanding and capability to utilize artificial intelligence (AI) incorporated technology has become a required basic skillset for the people living in today's information age, and various members of the university have also increasingly become aware of the need for AI education. Amidst such shifting societal demands, both domestic and international university libraries have recognized the users' need for educational content centered on AI, but a user-centered service that aims to provide personalized recommendations of digital AI educational content is yet to become available. It is critical while the demand for AI education amongst university students is progressively growing that university libraries acquire a clear understanding of user intention towards an AI educational content recommender system and the potential factors contributing to its success. This study intended to ascertain the factors affecting acceptance of such system, using the Extended Technology Acceptance Model with added variables - innovativeness, self-efficacy, social influence, system quality and task-technology fit - in addition to perceived usefulness, perceived ease of use, and intention to use. Quantitative research was conducted via online research surveys for university students, and quantitative research was conducted through written interviews of university librarians. Results show that all groups, regardless of gender, year, or major, have the intention to use the AI-related Educational Content Recommendation System, with the task suitability factor being the most dominant variant to affect use intention. University librarians have also expressed agreement about the necessity of the recommendation system, and presented budget and content quality issues as realistic restrictions of the aforementioned system.

• Information Systems Review
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• v.17 no.3
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• pp.95-111
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• 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 Korea Society of Computer and Information
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• v.14 no.9
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• pp.85-94
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• 2009

It is becoming increasingly difficult for learners to find the lectures they are looking for. In turn, the ability to find the particular lecture sought by the learner in an accurate and prompt manner has become an important issue in e-Learning. To deal this issue, in this paper. we present a collaborative approach to provide personalized recommendations of online video lectures. The proposed approach first identifies candidated video lectures that will be of interest to a certain user. Partitioned collaborative filtering is employed as an approach in order to generate neighbor learners and predict learners'preferences for the lectures. Thereafter, Attribute-based filtering is employed to recommend a final list of video lectures that the target user will like the most.

• Journal of Service Research and Studies
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• v.13 no.3
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• pp.82-104
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• 2023

This study analyzes the effect of users' experiences using AI chatbot services on relationship quality and behavioral intention. For the study, a survey was conducted on users who experienced AI chatbot services, and the research hypothesis was verified by analyzing the final 299 copies of valid data. As a result of the analysis, it was confirmed that satisfaction and trust, which are the relationship quality dimensions of AI chatbot service, were formed in users through the cognitive experience, emotional experience, and relational experience. In addition, it was confirmed that satisfaction and trust have a positive effect on the intention to continue using and recommending AI chatbot services, which correspond to the level of consumers' behavioral intentions, respectively. In addition, in terms of relationship quality, it was significant in all paths of the road of behavior, but in satisfaction, the path coefficient of the road of continuous use of AI chatbot and recommended road was significantly higher than the path coefficient in trust. This study provided a theoretical foundation that the relationship with relationship quality that affects behavioral intention also affects AI chatbot services in the online environment, and it is significant in that it suggests that relationship quality is an important mediating factor in establishing long-term relationships with consumers.

• The Journal of Korean Association of Computer Education
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• v.21 no.5
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• pp.61-68
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• 2018

Collaborative filtering is a fundamental technique implemented in many commercial recommender systems and provides a successful service to online users. This technique recommends items by referring to other users who have similar rating records to the current user. Hence, similarity measures critically affect the system performance. This study addresses problems of previous similarity measures and suggests a new similarity measure. The proposed measure reflects the subjectivity or vagueness of user ratings and the users' rating behavior by using fuzzy logic. We conduct experimental studies for performance evaluation, whose results show that the proposed measure demonstrates outstanding performance improvements in terms of prediction accuracy and recommendation accuracy.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.05a
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• pp.265-272
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• 2002

기존의 인터넷 웹사이트에서는 사용자의 만족을 극대화시키기 위하여 사용자별로 개인화 된 서비스를 제공하는 협력적 필터링 방식을 적용하고 있다 협력적 여과 기술은 비슷한 선호도를 가지는 사용자들과의 상관관계를 기반으로 취향에 맞는 아이템을 예측하여 특정 사용자에게 추천하여준다. 그러나 협력적 필터링은 추천을 받기 위해서 특정 수 이상의 아이템에 대한 평가를 요구하며, 또한 전체 사용자에 대해 단지 비슷한 선호도를 가지는 일부 사용자 정보에 의지하여 추천함으로써 나머지 사용자 정보를 무시하는 경향이 있다. 그러나 나머지 사용자 정보에도 추천을 위한 유용한 정보가 숨겨져 있다. 우리는 이러한 숨겨진 유용한 추천 정보를 발견하기 위하여 본 논문에서는 협력적 여과 방식과 함께 데이터 마이닝(Data Mining)에서 사용되는 연관 규칙(Association Rule)을 추천에 사용한다. 연관 규칙은 한 항목 그룹과 다른 항목 그룹 사이에 존재하는 연관성을 규칙(Rule)의 형태로 표현한 것이다. 이와 같이 생성된 연관 규칙은 개인 구매도 분석, 상품의 교차 매매(Cross-Marketing), 카탈로그 디자인, 염가 매출품(Loss Leader)분석, 상품 진열, 구매 성향에 따른 고객 분류 다양하게 사용되고 있다. 그러나 이런 연관 규칙은 추천 시스템에서 잘 응용되지 못하고 있는 실정이다. 본 논문에서 우리는 연관 규칙을 추천 시스템에 적용해, 항목 그룹 사이에 연관성을 유도함으로써 추천에 효율적으로 사용할 수 있음을 보였다. 즉 전체 사용자의 히스토리(History) 정보를 기반으로 아이템 사이의 연관 규칙을 유도하고 협력적 여과 방식과 함께 보조적으로 연관 규칙을 추천을 위해 사용함으로써 추천 시스템에 효율성을 높였다. 구축, 각종 전자문서 생성, 전자 결제, 온라인 보험 가입, 해운 선용품 판매 및 관련 정보 제공 등 해운 거래를 위한 종합적인 서비스가 제공되어야 한다. 이를 위해, 본문에서는 e-Marketplace의 효율적인 연계 방안에 대해 해운 관련 업종별로 제시하고 있다. 리스트 제공형, 중개형, 협력형, 보완형, 정보 연계형 등이 있는데, 이는 해운 분야에서 사이버 해운 거래가 가지는 문제점들을 보완하고 업종간 협업체제를 이루어 원활한 거래를 유도할 것이다. 그리하여 우리나라가 동북아 지역뿐만 아니라 세계적인 해운 국가 및 물류 ·정보 중심지로 성장할 수 있는 여건을 구축하는데 기여할 것이다. 나타내었다.약 1주일간의 포르말린 고정이 끝난 소장 및 대장을 부위별, 별 종양개수 및 분포를 자동영상분석기(Kontron Co. Ltd., Germany)로 분석하였다. 체의 변화, 장기무게, 사료소비량 및 마리당 종양의 개수에 대한 통계학적 유의성 검증을 위하여 Duncan's t-test로 통계처리 하였고, 종양 발생빈도에 대하여는 Likelihood ration Chi-square test로 유의성을 검증하였다. C57BL/6J-Apc$^{min/+}$계 수컷 이형접합체 형질전환 마우스에 AIN-76A 정제사료만을 투여한 대조군의 대장선종의 발생률은 84%(Group 3; 21/25례)로써 I3C 100ppm 및 300ppm을 투여한 경우에 있어서는 각군 모두 60%(Group 1; 12/20 례, Group 2; 15/25 례)로 감소하는 경향을 나타내었다. 대장선종의 마리당 발생개수에 있어서는 C57BL/6J-Apc$^{min/+}$계 수컷 이형접합체 형질전환 마우스에 AIN-76A 정제사료