• International journal of advanced smart convergence
• /
• 제12권4호
• /
• pp.202-207
• /
• 2023

In this paper, we discuss the design and implementation of a recommendation platform actually built in the field. We survey deep learning-based recommendation models that are effective in reflecting individual user characteristics. The recently proposed RNN-based sequential recommendation models reflect individual user characteristics well. The recommendation platform we proposed has an architecture that can collect, store, and process big data from a company's commercial services. Our recommendation platform provides service providers with intuitive tools to evaluate and apply timely optimized recommendation models. In the model evaluation we performed, RNN-based sequential recommendation models showed high scores.

• 한국디자인학회:학술대회논문집
• /
• 한국디자인학회 2004년도 춘계 학술발표대회 논문집
• /
• pp.294-295
• /
• 2004

• Journal of Information Processing Systems
• /
• 제19권5호
• /
• pp.576-589
• /
• 2023

To upgrade home style recommendations and user satisfaction, this paper proposes a personalized and optimized recommendation algorithm for interior design style based on local social network, which includes data acquisition by three-dimensional (3D) model, home-style feature definition, and style association mining. Through the analysis of user behaviors, the user interest model is established accordingly. Combined with the location-based social network of association rule mining algorithm, the association analysis of the 3D model dataset of interior design style is carried out, so as to get relevant home-style recommendations. The experimental results show that the proposed algorithm can complete effective analysis of 3D interior home style with the recommendation accuracy of 82% and the recommendation time of 1.1 minutes, which indicates excellent application effect.

• 한국멀티미디어학회논문지
• /
• 제22권2호
• /
• pp.260-270
• /
• 2019

As the growth of developing Islamic countries, Muslims are into the world. The most important thing for Muslims to purchase food, ingredient, cosmetics and other products are whether they were certified as 'Halal'. With the increasing number of Muslim tourists and residents in Korea, Halal restaurants and markets are on the rise. However, the service that provides information on Halal restaurants and markets in Korea is very limited. Especially, the application of recommendation system technology is effective to provide Halal restaurant information to users efficiently. The profiling of Halal restaurant information should be preceded by design of recommendation system, and design of recommendation algorithm is most important part in designing recommendation system. In this paper, an Intelligent Recommendation Service system for offering Halal food (IRSH) based on dynamic profiles was proposed. The proposed system recommend a customized Halal restaurant, and proposed recommendation algorithm uses hybrid filtering which is combined by content-based filtering, collaborative filtering and location-based filtering. The proposed algorithm combines several filtering techniques in order to improve the accuracy of recommendation by complementing the various problems of each filtering. The experiment of performance evaluation for comparing with existed restaurant recommendation system was proceeded, and result that proposed IRSH increase recommendation accuracy using Halal contents was deducted.

• 감성과학
• /
• 제9권spc3호
• /
• pp.179-185
• /
• 2006

본 연구는 다른 분야에서 성공적으로 활용되고 있는 다양한 추천 기법들을 비교하는 사례 연구를 통해 더욱 효과적인 디자인 개인화 서비스 개발의 기회를 모색하고자 하였다. 우선, 문헌연구를 통하여 '컨텐츠 기반 기법', '협력적 필터링 기법', 그리고 '인구통계적 필터링 기법'과 같은 대표적인 추천 기법들의 특징과 장단점을 고찰하였다. 다음으로 이러한 기법들이 디자인과 같은 컨텐츠를 대상으로 적용되었을 때 예상되는 추천 정확성을 분석하기 위해 실험을 실시하였다. 그 결과, 인구통계적 필터링 기법은 나머지 기법에 비해서 비교적 낮은 정확성을 보였으며 컨텐츠 기반 기법이 가장 좋은 높은 추천 정확성을 나타내었다. 아울러 협력적 필터링 기법은 참여자들의 수가 증가할수록 좀 더 높은 추천 정확성을 나타냄을 알 수 있었다. 결론적으로 디자인 추천 서비스는 컨텐츠 기반 기법이나 협력적 필터링 기법의 적용을 통해 그 추천 정확성을 향상시킬 수 있으며 대상 사용자의 수가 일정 수준 이상으로 증가된다면 협력적 필터링 기법이 가장 우수한 효율을 나타낼 가능성이 높음을 제시하였다.

• 디지털융복합연구
• /
• 제14권8호
• /
• pp.313-318
• /
• 2016

현대사회에서 추천 서비스는 클라이언트-서버 기반의 인터넷 서비스에서 소셜 네트워킹으로 변화되고 있다. 특히 최근에는 크라우드소싱과 소셜 네트워킹을 통하여 사용자에게 개인화 추천을 서비스하고 있다. 소셜 네트워크 기반 시스템은 메모리와 모델 기반 협력적 필터링을 이용한 추천 서비스 제공 방식과 목적에 따라 분류할 수 있다. 이에 본 논문에서는 소셜 네트워크 기반의 {사용자-연관 디자인} 행렬을 이용한 감성 디자인 추천을 제안한다. 제안하는 방법은 소셜 네트워크 기반에서 {사용자-연관 디자인} 행렬을 구성하고 메모리 기반 협력적 필터링을 이용하여 감성 디자인을 추천한다. 제안한 방법의 성능평가는 정확도와 재현율 검증을 진행한다. 정확도의 검증은 소셜 네트워크 기반의 추천 적용유무에 따른 F-measure를 사용한다.

• 패션비즈니스
• /
• 제25권5호
• /
• pp.25-37
• /
• 2021

As the online shopping market grows, channels in the mobile shopping environment have become increasingly diverse as a wide variety of products are introduced every day. This study investigated the effects of the self-construal type, mobile product recommendation system type, and fashion product type on purchase intention. The experimental design of this study was a 2 (self-construal type: independent vs. interdependent) × 2 (product recommendation system: bestseller vs. content-based) × 2 (fashion product type: utilitarian vs. hedonic) 3-way mixed ANOVA. Women (n = 387) in their 20 to 30s residing in Seoul and the Gyeonggi area participated in the study. The data were analyzed with the SPSS 24 program and 3-way ANOVA and simple main effects analyses were conducted. The results were as follows. First, self-construal, product recommendation, and fashion product types had a statistically significant impact on purchase intention. Second, fashion product and consumers' self-construal types had significant interaction effects on purchase intention. Finally, product recommendation and fashion product and self-construal types showed significant 3-way interaction effects on purchase intention. The study confirmed an interaction between the self-construal, type of product recommendation system, and the type of fashion product used in influencing purchase intention.

• 한국감성과학회:학술대회논문집
• /
• 한국감성과학회 2004년도 추계학술대회 논문집
• /
• pp.42-42
• /
• 2004

• Journal of Information Technology Applications and Management
• /
• 제26권5호
• /
• pp.57-65
• /
• 2019

Recommendation Systems are information technologies that E-commerce merchants have adopted so that online shoppers can receive suggestions on items that might be interesting or complementing to their purchased items. These systems stipulate valuable assistance to the user's purchasing decisions, and provide quality of push service. Traditionally, Recommendation Systems have been designed using a centralized system, but information service is growing vast with a rapid and strong scalability. The next generation of information technology such as Cloud Computing and Big Data Environment has handled massive data and is able to support enormous processing power. Nevertheless, analytic technologies are lacking the different capabilities when processing big data. Accordingly, we are trying to design a conceptual service model with a proposed new algorithm and user adaptation on dynamic recommendation service for big data environment.

• 한국컴퓨터정보학회논문지
• /
• 제22권7호
• /
• pp.125-131
• /
• 2017

It is not easy for the user to find the information that is appropriate for the user among the suddenly increasing information in recent years. One of the ways to help individuals make decisions in such a lot of information is the recommendation system. Although there are many recommendation methods for such recommendation systems, a representative method is collaborative filtering. In this paper, we design and implement the movie recommendation system on user-based collaborative filtering of apache mahout. In addition, Pearson correlation coefficient is used as a method of measuring the similarity between users. We evaluate Precision and Recall using the MovieLens 100k dataset for performance evaluation.