• 디지털융복합연구
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• 제17권11호
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• pp.201-206
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• 2019

온라인에서 보험 정보를 찾는 이용자들이 많은 반면, 보험사 웹 사이트 콘텐츠 추천 연구 사례는 많지 않았으므로 본 연구에서는 보험사 웹 사이트의 페이지 방문 이력을 활용하여 사용자에게 선호 가능성이 높은 페이지 추천 시스템을 제안하였다. 데이터는 웹 브라우저 이용 시 발생하는 클라이언트 사이트 스토리지(Client-side storage)를 활용하여 수집하였으며, 추천 기술로는 협업 필터링(Collaborative filtering)을 연구에 적용하였다. 실험을 실시한 결과 방문여부를 의미하는 이진화된 데이터를 사용한 자카드 인덱스(Jaccard index) 기반의 아이템 기반 협업 필터링(Item-based collaborative, IBCF)에서 좋은 성능을 나타내었다. 향후에는 아이템에 가중치를 부여한 추천 기술을 연구하여, 기업에서 사용 시 마케팅 전략에 부합하는 콘텐츠 추천 시스템을 구현할 수 있을 것이다.

• 한국컴퓨터정보학회논문지
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• 제23권7호
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• pp.11-17
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• 2018

In this paper, we propose how to improve the lack of diversity in collaborative filtering, using tag scores contained in items rather than ratings of items. Collaborative filtering has excellent performance among recommendation system, but it is evaluated as lacking diversity. In order to solve this problem, this paper proposes a method for supplementing diversity lacking in collaborative filtering by using tags. By using tags that can be used universally without using the characteristics of specific articles in a recommendation system, The proposed method can be used.

• 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.

• 한국경영과학회지
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• 제29권2호
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• pp.59-75
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• 2004

Recommendation is a personalized information filtering technology to help customers find which products they would like to purchase. Collaborative filtering works by matching customer preferences to other customers in making recommendations. But collaborative filtering based recommendations have two major limitations, sparsity and scalability. To overcome these problems we suggest using adjusted product hierarchy, grain. This methodology focuses on dimensionality reduction and uses a marketer's specific knowledge or experience to improve recommendation quality. The qualify of recommendations using each grain is compared with others by several experimentations. Experiments present that the usage of a grain holds the promise of allowing CF-based recommendations to scale to large data sets and at the same time produces better recommendations. In addition. our methodology is proved to save the computation time by 3∼4 times compared with collaborative filtering.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권9호
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• pp.4684-4705
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• 2019

Collaborative filtering recommender systems are vulnerable to shilling attacks in which malicious users may inject biased profiles to promote or demote a particular item being recommended. To tackle this problem, many robust collaborative recommendation methods have been presented. Unfortunately, the robustness of most methods is improved at the expense of prediction accuracy. In this paper, we construct a robust Bayesian probabilistic matrix factorization model for collaborative filtering recommender systems by incorporating the detection of user anomaly rating behaviors. We first detect the anomaly rating behaviors of users by the modified K-means algorithm and target item identification method to generate an indicator matrix of attack users. Then we incorporate the indicator matrix of attack users to construct a robust Bayesian probabilistic matrix factorization model and based on which a robust collaborative recommendation algorithm is devised. The experimental results on the MovieLens and Netflix datasets show that our model can significantly improve the robustness and recommendation accuracy compared with three baseline methods.

• 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.

• 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.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 2002년도 춘계공동학술대회
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• pp.320-325
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• 2002

Today many companies offer millions of products to customers. They are faced with a problem to choose particular products . In response to this problem a new marking strategy, recommendation has emerged. Among recommendation technologies collaborative filtering is most preferred. But the performance degrades with the number of customers and products. Namely, collaborative filtering has two major limitations, sparsity and scalability. To overcome these problems we introduced a new recommendation methodology using adjusted product hierarchy, grain. This methodology focuses on dimensionality reduction to improve recommendation quality and uses a marketer's specific knowledge or experience. In addition, it uses a new measure in the neighborhood formation step which is the most important one in recommendation process.

• 한국정보통신학회논문지
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• 제24권8호
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• pp.970-977
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• 2020

협업 필터링 추천 기법은 전자상거래 기업들이 추천시스템을 도입한 이래로 가장 널리 사용되고 있다. 그러나 온라인에서 상품이나 콘텐츠의 구매가 일상화되면서 단순히 구매 고객의 평점만을 사용하는 추천 방식으로는 추천의 정확성이 낮아지는 문제점이 발생하였다. 본 논문에서는 추천의 정확성을 향상시키기 위해, 상품 리뷰를 분석하고 이를 가중치로 사용한 협업 필터링 추천 기법을 제안한다. 제안하는 기법은 상품에 대한 리뷰를 텍스트 마이닝 기법으로 정제하여 특징을 추출하고 감성 기반 분석을 통해 감성 점수를 산출한다. 사용자에게 더 나은 아이템을 추천하기 위해 산출된 점수를 아이템 예측 값 계산 시 가중치로 사용한다. 실험을 통해 전통적인 협업 필터링 기법보다 제안하는 기법의 정확성이 향상되는 것을 확인할 수 있었다.

• 지능정보연구
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• 제9권2호
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• pp.51-63
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• 2003

Collaborative filtering is one of the methodologies that are most widely used for recommendation system. It is based on a data matrix of each customer's preferences of products. There could be a lot of missing values in such preference data matrix. This incomplete data is one of the reasons to deteriorate the accuracy of recommendation system. There are several treatments to deal with the incomplete data problem such as case deletion and single imputation. Those approaches are simple and easy to implement but they may provide biased results. Multiple imputation method imputes m values for each missing value. It overcomes flaws of single imputation approaches through considering the uncertainty of missing values. The objective of this paper is to suggest multiple imputation-based collaborative filtering approach for recommendation system to improve the accuracy in prediction performance. The experimental works show that the proposed approach provides better performance than the traditional Collaborative filtering approach, especially in case that there are a lot of missing values in dataset used for recommendation system.