• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.05a
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• pp.232-239
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• 2005

Collaborative-filtering-enabled Web sites that recommend books, CDs, movies, and so on, have become very popular on the Internet. Such sites recommend items to a user on the basis of the opinions of other users with similar tastes. This paper discuss an approach to collaborative filtering based on the Simple Bayesian and apply this model to two variants of the collaborative filtering. One is user-based collaborative filtering, which makes predictions based on the users' similarities. The other is item-based collaborative filtering which makes predictions based on the items' similarities. To evaluate the proposed algorithms, this paper used a database of movie recommendations. Empirical results show that the proposed Bayesian approaches outperform typical correlation-based collaborative filtering algorithms.

• 한국경영정보학회:학술대회논문집
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• 2007.06a
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• pp.576-581
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• 2007

The purpose of this paper is to strengthen trust on the automated collaborative filtering system. Automated collaborative filtering system is quickly becoming a popular technique for recommendation system. This elaborative methodology contributes for reducing information overload and the result becomes index of users' preference. In addition, it can be applied to various industries in various fields. After it collaborative filtering system was developed, many researches are executed to enhance credibility and to apply in various fields. Among these diverse systems, collaborative filtering system which uses Pearson correlation coefficient is most common in many researches. In this paper, we proposed new process diagram of collaborative filtering algorithm and new factors which should improve the credibility of system. In addition, the effects and relationships are also tested.

• Journal of the Korean Institute of Plant Engineering
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• v.23 no.4
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• pp.57-64
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• 2018

User-based collaborative filtering is a method of recommending an item to a user based on the preference of the neighbor users who have similar purchasing history to the target user. User-based collaborative filtering is based on the fact that users are strongly influenced by the opinions of other users with similar interests. Item-based collaborative filtering is a method of recommending an item by comparing the similarity of the user's previously preferred items. In this study, we create a recommendation model using user-based collaborative filtering and item-based collaborative filtering with consumer's consumption data. Collaborative filtering is performed by using RFM (recency, frequency, and monetary) technique with purchasing data to recommend items with high purchase potential. We compared the performance of the recommendation system with the purchase amount and the performance when applying the RFM method. The performance of recommendation system using RFM technique is better.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.11a
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• pp.333-337
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• 2000

성공적인 사이트를 위한 필수적인 요소로 각광받고 있는 collaborative filtering 기술은 정보의 과부하를 줄일 수 있고 고객에 대한 충성도를 높여주는 효과로 인해 많은 사이트에 적용되어 운용되고 있다. 이 논문에서는 collaborative filtering 적용 포기에 발생하는 정보의 부족으로 인한 정확도 저하를 막기 위해 상품간 연관성을 이용한 결측티 예측 방안을 제안한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.8
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• pp.2948-2966
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• 2014

To promote recommendation services through prediction quality, some privacy-preserving collaborative filtering solutions are proposed to make e-commerce parties collaborate on partitioned data. It is almost probable that two parties hold ratings for the same users and items simultaneously; however, existing two-party privacy-preserving collaborative filtering solutions do not cover such overlaps. Since rating values and rated items are confidential, overlapping ratings make privacy-preservation more challenging. This study examines how to estimate predictions privately based on partitioned data with overlapped entries between two e-commerce companies. We consider both user-based and item-based collaborative filtering approaches and propose novel privacy-preserving collaborative filtering schemes in this sense. We also evaluate our schemes using real movie dataset, and the empirical outcomes show that the parties can promote collaborative services using our schemes.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2004.11a
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• pp.203-210
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• 2004

Due to the explosion of e-commerce, recommender systems are rapidly becoming a core tool to accelerate cross-selling and strengthen customer loyalty. There are two prevalent approaches for building recommender systems - content-based recommending and collaborative filtering. Collaborative filtering recommender systems have been very successful in both information filtering domains and e-commerce domains, and many researchers have presented variations of collaborative filtering to increase its performance. However, the current research on recommendation has paid little attention to the use of time related data in the recommendation process. Up to now there has not been any study on collaborative filtering to reflect changes in user interest. This paper proposes dynamic fuzzy clustering algorithm and apply it to collaborative filtering algorithm for dynamic recommendations. The proposed methodology detects changes in customer behavior using the customer data at different periods of time and improves the performance of recommendations using information on changes. The results of the evaluation experiment show the proposed model's improvement in making recommendations.

• 한국IT서비스학회:학술대회논문집
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• 2003.11a
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• pp.223-232
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• 2003

추천시스템을 설계하는 방법에는 크게 Content-Based Filtering 기법과 Collaborative Filtering 기법이 있다. 이 중 Collaborative Filtering 기법은 사용자가 아직 평가하지 못한 상품에 대한 예측값을 계산할 때, 나와 유사한 상품선호를 갖고 있는 사람들이 그 상품에 대해 평가한 점수를 활용하는 방법이다. 하지만 순수한 Collaborative Filtering 방법은 일반적으로 알려진 Data Sparsity의 문제, First Rater의 문제뿐만 아니라 예측값의 부정확성과 기하급수적 계산량의 증가로 실제구현이 어렵다는 문제점을 가지고 있다. 본 연구에서는 이러한 'Collaborative filtering' 시스템의 문제들 중 예측의 부정확성과 실제 구현의 어려움을 해결할 수 있는 방법으로 군집분석을 적용해 보았다. 특히 본 연구에서는 군집을 나눌 때, 실제 추천이 이루어지는 상품 도메인이 아닌, 그 상품도메인과 비슷한 선호의 기준을 가지고 선택하게 되는 '선택의 상관관계'가 높은 '이웃 상품도메인'에서 사용자들의 군집을 나누고 이를 실제 추천이 이루어지는 상품도메인에 적용하는 방식을 사용하였다.

• Proceedings of the IEEK Conference
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• 2003.11b
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• pp.175-178
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• 2003

Two defects have been pointed out in existing user-based collaborative filtering such as sparsity and scalability, and the research has been also made progress, which tries to improve these defects using item-based collaborative filtering. Actually there were many results, but the problem of sparsity still remains because of being based on an explicit data. In addition, the issue has been pointed out. which attributes of item arenot reflected in the recommendation. This paper suggests a recommendation method using nave Bayesian algorithm in hybrid user and item-based collaborative filtering to improve above-mentioned defects of existing item-based collaborative filtering. This method generates a similarity table for each user and item, then it improves the accuracy of prediction and recommendation item using naive Bayesianalgorithm. It was compared and evaluated with existing item-based collaborative filtering technique to estimate the accuracy.

• Journal of Information Technology Services
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• v.13 no.2
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• pp.173-184
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• 2014

As social network services has become one of the most successful web-based business, recommendation in social network sites that assist people to choose various products and services is also widely adopted. Collaborative Filtering is one of the most widely adopted recommendation approaches, but recommendation technique that use explicit or implicit social network information from social networks has become proposed in recent research works. In this paper, we reviewed and compared research works about recommendation using social network analysis and collaborative filtering in social network sites. As the results of the analysis, we suggested the trends and implications for future research of recommendation in SNSs. It is expected that graph-based analysis on the semantic social network and systematic comparative analysis on the performances of social filtering and collaborative filtering are required.

• Journal of KIISE:Software and Applications
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• v.31 no.3
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• pp.332-342
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• 2004

With the explosive growth of information in our real life, information filtering is quickly becoming a popular technique for reducing information overload. Information filtering technique is divided into two categories: content-based filtering and collaborative filtering (or social filtering). Content-based filtering selects the information based on contents; while collaborative filtering combines the opinions of other persons to make a prediction for the target user. In this paper, we describe a new filtering approach that seamlessly combines content-based filtering and collaborative filtering to take advantages from both of them, where a technique using user profiles efficiently on the collaborative filtering framework is introduced to predict a user's preference. The proposed approach is experimentally evaluated and compared to conventional filtering. Our experiments showed that the proposed approach not only achieved significant improvement in prediction quality, but also dealt with new users well.