• 제목/요약/키워드: collaborative filtering system

검색결과 501건 처리시간 0.024초

추천시스템을 위한 내용기반 필터링과 협력필터링의 새로운 결합 기법 (A New Approach Combining Content-based Filtering and Collaborative Filtering for Recommender Systems)

  • 김병만;이경;김시관;임은기;김주연
    • 한국정보과학회논문지:소프트웨어및응용
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    • 제31권3호
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    • pp.332-342
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    • 2004
  • 엄청난 속도로 증가하고 있는 정보의 홍수 시대에서는 정보들을 선별하기 위하여 정보 필터링기법이 필요하다. 정보 필터링은 내용 기반 방법과 협력에 의한 방법으로 분류할 수 있다. 내용 기반 기법에서는 내용에 기반을 두어 정보를 추출하는 반면 협력 기법은 다른 사람들의 의견을 이용하게 된다. 본 논문에서는 기존 협력 필터링 방법의 문제점을 해결하기 위한 방법의 일환으로 내용 기반 기법과 협력 기법을 보다 유기적으로 결합시키는 연구를 수행하였다. 이를 위해 협력 필터링 틀을 그대로 유지하면서 사용자 프로파일을 효과적으로 이용하는 방법을 제안하였다. 또한, 본 논문에서 제시한 기법을 실험적으로 분석하고 기존의 필터링 기법과 비교하였다. 실험 결과, 본 방법이 예측 질 면에서 상당한 성능 향상이 있었고 새로운 사용자에게도 보다 나은 추천을 할 수 있음을 알 수 있었다.

Using Experts Among Users for Novel Movie Recommendations

  • Lee, Kibeom;Lee, Kyogu
    • Journal of Computing Science and Engineering
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    • 제7권1호
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    • pp.21-29
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    • 2013
  • The introduction of recommender systems to existing online services is now practically inevitable, with the increasing number of items and users on online services. Popular recommender systems have successfully implemented satisfactory systems, which are usually based on collaborative filtering. However, collaborative filtering-based recommenders suffer from well-known problems, such as popularity bias, and the cold-start problem. In this paper, we propose an innovative collaborative-filtering based recommender system, which uses the concepts of Experts and Novices to create fine-grained recommendations that focus on being novel, while being kept relevant. Experts and Novices are defined using pre-made clusters of similar items, and the distribution of users' ratings among these clusters. Thus, in order to generate recommendations, the experts are found dynamically depending on the seed items of the novice. The proposed recommender system was built using the MovieLens 1 M dataset, and evaluated with novelty metrics. Results show that the proposed system outperforms matrix factorization methods according to discovery-based novelty metrics, and can be a solution to popularity bias and the cold-start problem, while still retaining collaborative filtering.

개인별 상품추천시스템, WebCF-PT: 웹마이닝과 상품계층도를 이용한 협업필터링 (A Personalized Recommender System, WebCF-PT: A Collaborative Filtering using Web Mining and Product Taxonomy)

  • 김재경;안도현;조윤호
    • Asia pacific journal of information systems
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    • 제15권1호
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    • pp.63-79
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    • 2005
  • Recommender systems are a personalized information filtering technology to help customers find the products they would like to purchase. Collaborative filtering is known to be the most successful recommendation technology, but its widespread use has exposed some problems such as sparsity and scalability in the e-business environment. In this paper, we propose a recommendation system, WebCF-PT based on Web usage mining and product taxonomy to enhance the recommendation quality and the system performance of traditional CF-based recommender systems. Web usage mining populates the rating database by tracking customers' shopping behaviors on the Web, so leading to better quality recommendations. The product taxonomy is used to improve the performance of searching for nearest neighbors through dimensionality reduction of the rating database. A prototype recommendation system, WebCF-PT is developed and Internet shopping mall, EBIB(e-Business & Intelligence Business) is constructed to test the WebCF-PT system.

심층신경망 기반의 뷰티제품 추천시스템 (Deep Neural Network-Based Beauty Product Recommender)

  • 송희석
    • Journal of Information Technology Applications and Management
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    • 제26권6호
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    • pp.89-101
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    • 2019
  • Many researchers have been focused on designing beauty product recommendation system for a long time because of increased need of customers for personalized and customized recommendation in beauty product domain. In addition, as the application of the deep neural network technique becomes active recently, various collaborative filtering techniques based on the deep neural network have been introduced. In this context, this study proposes a deep neural network model suitable for beauty product recommendation by applying Neural Collaborative Filtering and Generalized Matrix Factorization (NCF + GMF) to beauty product recommendation. This study also provides an implementation of web API system to commercialize the proposed recommendation model. The overall performance of the NCF + GMF model was the best when the beauty product recommendation problem was defined as the estimation rating score problem and the binary classification problem. The NCF + GMF model showed also high performance in the top N recommendation.

A Social Travel Recommendation System using Item-based collaborative filtering

  • 김대호;송제인;유소엽;정옥란
    • 인터넷정보학회논문지
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    • 제19권3호
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    • pp.7-14
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    • 2018
  • As SNS(Social Network Service) becomes a part of our life, new information can be derived through various information provided by SNS. Through the public timeline analysis of SNS, we can extract the latest tour trends for the public and the intimacy through the social relationship analysis in the SNS. The extracted intimacy can also be used to make the personalized recommendation by adding the weights to friends with high intimacy. We apply SNS elements such as analyzed latest trends and intimacy to item-based collaborative filtering techniques to achieve better accuracy and satisfaction than existing travel recommendation services in a new way. In this paper, we propose a social travel recommendation system using item - based collaborative filtering.

오프라인 쇼핑몰에서 고객의 과거 구매 패턴을 활용한 아이템 기반 협업필터링 성능 개선에 관한 연구 (Improvement of Item-Based Collaborative Filtering by Applying Each Customer's Purchase Patterns in Offline Shopping Malls)

  • 정석봉
    • Journal of Information Technology Applications and Management
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    • 제24권4호
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    • pp.1-12
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    • 2017
  • Item-based collaborative filtering (IBCF) is an important technology that is widely used in recommender system of online shopping malls. It uses historical information to compute item-item similarity and make predictions. However, in offline shopping each customer's purchasing pattern can be occurred continuously and repeatedly due to time and space constraints contrast to online shopping. Those facts can make IBCF to have limitations from being applied to offline shopping malls directly. In order to improve the quality of recommendations made by IBCF in offline shopping mall, we propose an ensemble approach that considers both item-item similarity of IBCF and each customer's purchasing patterns which are modeled by item networks. Our experimental results show that this approach produces recommendation results superior to those of existing works such as pure IBCF or bestseller approaches.

협업적 필터링 및 퍼지시스템 기반 사용자 성향분석에 의한 영화평가 예측 시스템 (A Movie Rating Prediction System of User Propensity Analysis based on Collaborative Filtering and Fuzzy System)

  • 이수진;전태룡;백경동;김성신
    • 한국지능시스템학회논문지
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    • 제19권2호
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    • pp.242-247
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    • 2009
  • 지능형 추천 시스템은 사용자의 요청에 응답하는 수동적인 시스템이 아닌 사용자가 원하는 서비스를 제안하는 시스템으로서 최근 콘텐츠 서비스 분야에 많이 개발되고 있다. 이러한 지능형 추천 시스템은 콘텐츠 개인화 서비스에 응용되고 있으며 대표적인 추천기법으로 내용기반과 협업적 필터링 기법이 있다. 본 연구에서는 협업적 필터링 및 퍼지 시스템을 이용하여 추천 시스템의 기반 기술인 예측 시스템을 제안하였다. 제안한 예측 시스템은 사용자의 과거 영화평가 정보를 바탕으로 영화에 대한 평가점수를 예측한다. 영화평가 예측시스템의 성능은 영화 평가점수의 실제값과 예측값의 오차를 RMSE(root mean square error) 방법으로 계산한 후 기존의 영화평가 시스템 RMSE 값과 비교하여 평가하였다. 본 연구를 통해 제안한 영화평가 예측시스템이 추천 시스템의 기반 기술로서 활용이 가능하고 다른 멀티미디어 컨텐츠 서비스 추천에도 응용이 가능할 것으로 기대한다.

Design and Implementation of Collaborative Filtering Application System using Apache Mahout -Focusing on Movie Recommendation System-

  • Lee, Jun-Ho;Joo, Kyung-Soo
    • 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.

협업 여과 기반의 교육용 컨텐츠 추천 시스템 설계 (The Educational Contents Recommendation System Design based on Collaborative Filtering Method)

  • 이용준;이세훈;왕창종
    • 컴퓨터교육학회논문지
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    • 제6권2호
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    • pp.147-156
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    • 2003
  • 협업여과는 흥미 있어하는 제품이나 개인화된 자료, 항목을 제공하기 의해 전체 집단의 의견을 반영하는 전자상거래에서 일반적으로 이용되는 기술이다. 협업여과는 정확하고 신뢰할 수 있는 도구로 입증되어 여러 분야의 전자상거래 영역에서 활용되고 있으나 아직까지 교육분야에는 한정적으로 적용되고 있다. 본 논문에서는 교육용 컨텐츠 추천에 사용자의 평가 점수를 이용하는 협업여과 방식의 추천시스템을 설계하였으며, 사용자 정보를 이용하여 추천의 정확도를 향상시키기 위한 유사도 보정기법을 도입하였다. 평균절대오차(MAE)와 반응자작용특성(ROC)값을 이용하여 제안한 시스템이 기존의 협업여과방식보다 추천 효율이 우수함을 검증하였다.

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Auxiliary Stacked Denoising Autoencoder based Collaborative Filtering Recommendation

  • Mu, Ruihui;Zeng, Xiaoqin
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제14권6호
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    • pp.2310-2332
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    • 2020
  • In recent years, deep learning techniques have achieved tremendous successes in natural language processing, speech recognition and image processing. Collaborative filtering(CF) recommendation is one of widely used methods and has significant effects in implementing the new recommendation function, but it also has limitations in dealing with the problem of poor scalability, cold start and data sparsity, etc. Combining the traditional recommendation algorithm with the deep learning model has brought great opportunity for the construction of a new recommender system. In this paper, we propose a novel collaborative recommendation model based on auxiliary stacked denoising autoencoder(ASDAE), the model learns effective the preferences of users from auxiliary information. Firstly, we integrate auxiliary information with rating information. Then, we design a stacked denoising autoencoder based collaborative recommendation model to learn the preferences of users from auxiliary information and rating information. Finally, we conduct comprehensive experiments on three real datasets to compare our proposed model with state-of-the-art methods. Experimental results demonstrate that our proposed model is superior to other recommendation methods.