• 지능정보연구
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• 제14권2호
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• pp.63-82
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• 2008

추천시스템은 정보 과부하의 문제를 해결하기 위해 폭넓게 사용되어지고 있다. 지난 수십년 동안 다양한 추천시스템이 정보량이 그것을 처리할 수 있는 능력보다 더 빠르게 증가하게 됨에 따라 개발되어져 왔다. 이 같은 상황에서 본 연구의 목적은 기 개발된 추천시스템을 분석하여 시스템적 관점을 제공하고 이를 구현하는데 따르는 기본적인 이슈들을 밝히는 것이다. 이를 통하여 추천시스템의 개선을 위한 유용한 정보를 제안하며, 시스템 개발자들에게는 그러한 시스템을 개선하기 위한 아이디어를 제공하고자 한다. 특히 본 연구는 추천시스템의 이론적 관점에 집중하는데, 이를 위해 과거 추천시스템의 도메인과 목표, 주요 방법 및 평가 방법에 대해서 다루고자 하며, 이 결과는 통계치나 도표 등의 형태로 보이려고 한다.

• 한국컴퓨터정보학회논문지
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• 제18권2호
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• pp.107-116
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• 2013

본 연구에서는 소셜 음악 사이트에서 사용자들이 음악 아이템을 청취한 횟수와 생성한 태그 정보를 혼합하여 음악을 추천하는 시스템을 제안한다. 현재, 상용화된 음악 추천 시스템들은 주로 사용자의 청취 습관과 외부적인 선호도 입력값을 기반으로 음악을 추천하고 있다. 그러나 이 방식은 아직 음악을 청취한 사용자가 많지 않은 새로운 음악이나 청취 정보가 없는 새로운 사용자의 경우 추천하는 데 어려움이 있다. 이 문제를 해결하기 위해서 본 논문에서는 사용자가 선정한 키워드를 아이템에 부여하는 협업 태깅으로 생성된 태그 정보를 활용하였다. 태그의 의미를 파악하여 감정 표현의 정도에 따라 가중치를 부여한 뒤, 태그 점수와 청취 횟수를 혼합하여 음악 아이템의 선호도를 산출하였다. 이를 기반으로 사용자 프로파일을 생성하고 협업 필터링 알고리즘을 수행하였다. 제안하는 추천 방법의 효율성을 평가하기 위해서, 청취 습관 기반 추천, 태그 점수 기반 추천, 하이브리드 추천 방법의 세 가지 추천 방법에 대해서 정확도, 재현율, 그리고 F-measure를 계산하였다. 실험 결과에 대해 통계적 검증을 시행한 결과, 하이브리드 추천 방법이 다른 두 가지 방식보다 통계적으로 유의한 차이를 보여 성능이 우수한 것으로 나타났다.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제14권3호
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• pp.306-310
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• 2008

정보추천 서비스를 위한 선호도는 상황에 따라 달라질 수 있으므로, 정보추천 서비스를 제공하기 위해서는 먼저 사용자의 컨덱스트 정보를 알아야 한다. 본 논문은 모바일 환경에서 다수 사용자의 선호도를 고려한 추천 시스템을 제안하며, 음식점 추천에 이를 적용하고자 한다. 모바일 환경에서 개별 사용자의 선호도를 모델링하기 위해 베이지안 네트워크를 사용하였으며, 음식점 추천은 많은 경우 개별 사용자가 아닌 다수 사용자의 선호도를 고려해야 하므로, 본 논문에서는 개별 사용자의 선호도를 바탕으로 다수의 선호도를 획득하기 위해 다기준 의사결정방법인 AHP를 이용하였다. 실험을 위해서 10가지 서로 다른 상황에서 추천을 수행하였으며, 마지막으로 SUS 사용성 평가를 통해 제안하는 시스템의 사용성이 높게 평가되었음을 확인하였다.

• 한국의류학회지
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• 제48권3호
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• pp.485-500
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• 2024

This study was conducted to examine the effects of consumer evaluations on size recommendation services based on body information on consumer responses and the moderating effect of the level of information search. To analyze the research model, a total of 200 data were collected from August 18 to 24, 2022, targeting consumers who had experience with using size recommendation services based on body information. As a result of the research model analysis, it was confirmed that the compatibility, reliability, and convenience of the size recommendation services based on body information influenced attitude, which, in turn, influenced usage intention. In addition, In the case of the group subject to a low level of information search, the path through which compatibility and reliability influenced attitude was significant, but that of convenience was not. In the group featuring a high level of information search, the path through which reliability and convenience influenced attitude was significant, but that of compatibility was not. This study is meaningful in that it expanded research related to size recommendation services to the field of consumer behavior.

• 대한안전경영과학회지
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• 제10권4호
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• pp.309-314
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• 2008

Many studies on predicting and recommending information and products have been studying to meet customers' preference. Unnecessary information should be removed to satisfy customers' needs in massive information. The some information filtering methods to remove unnecessary information have been suggested but these methods have scarcity and scalability problems. Therefore, this paper explores a personalized recommendation system based on artificial neural network (ANN) to solve these problems. The insurance product recommendation is adapted as an example to demonstrate the proposed method. The proposed recommendation system is expected to recommended a suitable and personalized insurance products for customers' satisfaction.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2001년도 The Pacific Aisan Confrence On Intelligent Systems 2001
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• pp.192-197
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• 2001

A recommendation system tracks past actions of a group of users to make a recommendation to individual members of the group. The computer-mediated marketing and commerce have grown rapidly nowadays so the concerns about various recommendation procedures are increasing. We introduce a recommendation methodology by which e-commerce sites suggest new products of services to their customers. The suggested methodology is based on web log analysis, product taxonomy, and association rule mining. A product recommendation system is developed based on our suggested methodology and applied to a Korean internet shopping mall. The validity of our recommendation system is discussed with the analysis of a real internet shopping mall case.

• International Journal of Advanced Culture Technology
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• 제8권4호
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• pp.263-270
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• 2020

The growth of the mobile app markets has made it popular among people who recommend relevant information about restaurants. The recommendation service app based on AI Chatbot is that it can efficiently manage time and finances by making it easy for restaurant consumers to easily access the information they want anytime, anywhere. Eating out consumers use smartphone applications for finding restaurants, making reservations, and getting reviews and how to use them. In addition, social attention has recently been focused on the research of AI chatbot. The Chatbot is combined with the mobile messenger platform and enabling various services due to the text-type interactive service. It also helps users to find the services and data that they need information tersely. Applying this to restaurant recommendation services will increase the reliability of the information in providing personal information. In this paper, an artificial intelligence chatbot-based smartphone restaurant recommendation app using personalization information is proposed. The recommendation service app utilizes personalization information such as gender, age, interests, occupation, search records, visit records, wish lists, reviews, and real-time location information. Users can get recommendations for restaurants that fir their purpose through chatting using AI chatbot. Furthermore, it is possible to check real-time information about restaurants, make reservations, and write reviews. The proposed app uses a collaborative filtering recommendation system, and users receive information on dining out using artificial intelligence chatbots. Through chatbots, users can receive customized services using personal information while minimizing time and space limitations.

• Journal of Information Technology Applications and Management
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• 제26권5호
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• pp.57-65
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• 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
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• 제22권7호
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• pp.125-131
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• 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.

• 디지털산업정보학회논문지
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• 제15권3호
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• pp.69-78
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• 2019

The amount of data is increasing significantly as information and communication technology advances, mobile, cloud computing, the Internet of Things and social network services become commonplace. As the data grows exponentially, there is a growing demand for services that recommend the information that users want from large amounts of data. Collaborative filtering method is commonly used in information recommendation methods. One of the problems with collaborative filtering-based recommendation method is the cold start problem. In this paper, we propose a method to improve the cold start problem. That is, it solves the cold start problem by mapping the item evaluation data that does not exist to the initial user to the automatically generated data from the mobile app. We describe the main contents of the proposed method and explain the proposed method through the book recommendation scenario. We show the superiority of the proposed method through comparison with existing methods.