• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2002년도 봄 학술발표논문집 Vol.29 No.1 (B)
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• pp.244-246
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• 2002

The rapid increase of E-commerce market requires a solution to assist the buyer to find his or her interested items. The intelligent agent model is one of the approaches to help the buyers in purchasing items in outline market. In this paper, the user query processing model in the item recommendation agent is proposed. In the proposed model, the retrieval result is affected by the automatically generated queries from user preference information as well as the queries explicitly given by user. Therefore, the proposed model can provide the customized search results to each user.

• Management Science and Financial Engineering
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• 제12권2호
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• pp.71-85
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• 2006

Collaborative filtering, among other recommender systems, has been known as the most successful recommendation technique. However, it requires the user-item rating data, which may not be easily available. As an alternative, some collaborative filtering algorithms have been developed recently by utilizing the market basket data in the form of the binary user-item matrix. Viewing the recommendation scheme as a two-class classification problem, we proposed a new collaborative filtering scheme using a regularized discriminant analysis applied to the binary user-item data. The proposed discriminant model was built in terms of the major principal components and was used for predicting the probability of purchasing a particular item by an active user. The proposed scheme was illustrated with two modified real data sets and its performance was compared with the existing user-based approach in terms of the recommendation precision.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2003년도 춘계학술대회
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• pp.400-409
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• 2003

Collaborative filtering (CF) recommendation is a knowledge sharing technology for distribution of opinions and facilitating contacts in network society between people with similar interests. The main concerns of the CF algorithm are about prediction accuracy, speed of response time, problem of data sparsity, and scalability. In general, the efforts of improving prediction algorithms and lessening response time are decoupled. We propose a three-step CF recommendation model which is composed of profiling, inferring, and predicting steps while considering prediction accuracy and computing speed simultaneously. This model combines a CF algorithm with two machine learning processes, SOM (Self-Organizing Map) and CBR (Case Based Reasoning) by changing an unsupervised clustering problem into a supervised user preference reasoning problem, which is a novel approach for the CF recommendation field. This paper demonstrates the utility of the CF recommendation based on SOM cluster-indexing CBR with validation against control algorithms through an open dataset of user preference.

• Journal of Korea Artificial Intelligence Association
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• 제1권1호
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• pp.7-10
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• 2023

This study proposes an AI-based shoe recommendation model based on user clothing image data to solve the problem of the global fashion industry, which is worsening due to factors such as the economic downturn. Shoes are an important part of modern fashion, and this research aims to improve user satisfaction and contribute to economic growth through a generative AI-based shoe recommendation service. By utilizing generative AI in the personalized consumer market, we show the feasibility, efficiency, and improvements through an accessible web-based implementation. In conclusion, this study provides insights to help fulfill consumer needs in the ever-changing fashion market by implementing a generative AI-based shoe recommendation model.

• Journal of Information Processing Systems
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• 제20권3호
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• pp.360-374
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• 2024

A personalized course recommendation algorithm based on deep learning in an online education cloud platform is proposed to address the challenges associated with effective information extraction and insufficient feature extraction. First, the user potential preferences are obtained through the course summary, course review information, user course history, and other data. Second, by embedding, the word vector is turned into a low-dimensional and dense real-valued vector, which is then fed into the compressed interaction network-deep neural network model. Finally, considering that learners and different interactive courses play different roles in the final recommendation and prediction results, an attention mechanism is introduced. The accuracy, recall rate, and F1 value of the proposed method are 0.851, 0.856, and 0.853, respectively, when the length of the recommendation list K is 35. Consequently, the proposed strategy outperforms the comparison model in terms of recommending customized course resources.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2019년도 춘계학술대회
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• pp.502-503
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• 2019

리뷰 데이터 분석을 통해 추천을 하는 기존 시스템에서 사용자의 특성 혹은 상품 구매 취향와 같은 개인의 선호 세부 정보를 반영하지 않는 점을 보완하여 본 논문에서는 사용자가 상품을 검색하고 그 상품을 구매할 때 가장 중요하게 생각하는 기준을 선택하도록 하고, 이를 반영하여 분석함으로써 다양한 사용자에게 맞춤화된 추천 정보를 제공하는 시스템을 제안한다. 이는 사용자가 상품 구매 시 가장 큰 비중을 차지하는 기준을 토대로 가중치를 부여하여 감성분석을 수행하고 그 결과를 반영하여 상품 목록을 제공한다. 따라서, 상품 추천 정보에 사용자 개인의 선호도를 반영하였기 때문에 기존 추천 시스템을 통해 상품을 추천받는 것보다 효율적인 결과를 얻을 수 있을 것으로 사료된다.

• 한국인터넷방송통신학회논문지
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• 제23권5호
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• pp.1-8
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• 2023

교차 도메인 추천은 다른 도메인에 있는 관련 사용자 정보 데이터와 아이템 데이터를 공유하는 방법입니다. 주로 사용자 중복이 많은 온라인 쇼핑몰이나 유튜브, 넷플릭스와 같은 멀티미디어 서비스 컨텐츠에서 사용됩니다. K-means 클러스터링을 통해 사용자 데이터와 평점을 기반으로 군집화를 실시하여 임베딩을 생성합니다. 이 결과를 트랜스포머 네트워크를 통해 학습한 후 사용자 만족도를 예측합니다. 그런 다음 트랜스포머 기반 추천 모델을 사용하여 사용자에게 적합한 아이템을 추천합니다. 이 연구를 통해 추천함으로써 더 적은 시간적 비용으로 초기 사용자 문제를 예측하고 사용자들의 만족도를 높일 수 있다는 결과를 실험을 통해 보여주었습니다.

• Journal of Korea Artificial Intelligence Association
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• 제2권1호
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• pp.1-6
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• 2024

In this paper, we develop an AI-based recommendation system that matches the specifications of smartphones from company 'S'. The system aims to simplify the complex decision-making process of consumers and guide them to choose the smartphone that best suits their daily needs. The recommendation system analyzes five specifications of smartphones (price, battery capacity, weight, camera quality, capacity) to help users make informed decisions without searching for extensive information. This approach not only saves time but also improves user satisfaction by ensuring that the selected smartphone closely matches the user's lifestyle and needs. The system utilizes unsupervised learning, i.e. clustering (K-MEANS, DBSCAN, Hierarchical Clustering), and provides personalized recommendations by evaluating them with silhouette scores, ensuring accurate and reliable grouping of similar smartphone models. By leveraging advanced data analysis techniques, the system can identify subtle patterns and preferences that might not be immediately apparent to consumers, enhancing the overall user experience. The ultimate goal of this AI recommendation system is to simplify the smartphone selection process, making it more accessible and user-friendly for all consumers. This paper discusses the data collection, preprocessing, development, implementation, and potential impact of the system using Pandas, crawling, scikit-learn, etc., and highlights the benefits of helping consumers explore the various options available and confidently choose the smartphone that best suits their daily lives.

• 한국멀티미디어학회논문지
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• 제11권2호
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• pp.257-268
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• 2008

멀티미디어 추천시스템은 사용자의 선호도를 분석하여 멀티미디어 상품을 사용자에게 추천하는 시스템이다. 다양한 추천 기법들에서 가장 널리 사용되는 기법은 협동적 여과 방식이다. 그러나 협동적 여과는 정보 부족 문제와 초기 시작 문제가 존재한다. 선호도 정보가 적게 존재하면 유사 사용자 추출이 어려우며, 이러한 문제는 시스템을 처음 사용하는 새로운 사용자에게 더욱 심각한 문제를 발생시킨다. 본 논문에서는 정보 부족 문제를 해결하고 추천 정확도를 향상시키기 위해 사용자와 상품에 대한 속성 생성 기법을 제안한다. 본 논문에서 제안한 기법은 속성의 분포를 이용하여 추가 속성을 생성하고, 추가 속성을 포함한 변형된 데이터를 이용하여 상품을 추천한다. 여러 실험을 통해 제안된 기법의 효과를 확인하였다.

• ETRI Journal
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• 제29권2호
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• pp.169-178
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• 2007

With the advent of ubiquitous computing environments, it has become increasingly important for applications to take full advantage of contextual information, such as the user's location, to offer greater services to the user without any explicit requests. In this paper, we propose context-aware active services based on context-aware middleware for URC systems (CAMUS). The CAMUS is a middleware that provides context-aware applications with a development and execution methodology. Accordingly, the applications based on CAMUS respond in a timely fashion to contextual information. This paper presents the system architecture of CAMUS and illustrates the content recommendation and control service agents with the properties, operations, and tasks for context-aware active services. To evaluate CAMUS, we apply the proposed active services to a TV application domain. We implement and experiment with a TV content recommendation service agent, a control service agent, and TV tasks based on CAMUS. The implemented content recommendation service agent divides the user's preferences into common and specific models to apply other recommendations and applications easily, including the TV content recommendations.