• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.7
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• pp.887-892
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• 2005

Under pure P2P environments, collaborative filtering must be provided with only a few service items by real time information without accumulated data. However, in case of collaborative filtering with only a few service items collected locally, quality of recommended service becomes low. Therefore, it is necessary to research a method to improve quality of recommended service by users' context information. But because a great volume of users' context information can be recognized in a moment, there can be a scalability problem and there are limitations in supporting differentiated services according to fields and items. In this paper, we solved the scalability problem by clustering context information Per each service field and classifying il per each user, using SOM. In addition, we could recommend proper services for users by measuring the context information of the users belonging to the similar classification to the service requester among classified data and then using collaborative filtering.

• Journal of Digital Convergence
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• v.14 no.8
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• pp.313-318
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• 2016

The recommendation service is changing from client-server based internet service to social networking. Especially in recent years, it is serving recommendations with personalization to users through crowdsourcing and social networking. The social networking based systems can be classified depending on methods of providing recommendation services and purposes by using memory and model based collaborative filtering. In this study, we proposed the social network based sensibility design recommendation using associative user. The proposed method makes {user - associative design} matrix through the social network and recommends sensibility design using the memory based collaborative filtering. For the performance evaluation of the proposed method, recall and precision verification are conducted. F-measure based on recommendation of social networking is used for the verification of accuracy.

• Journal of KIISE:Software and Applications
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• v.33 no.11
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• pp.953-964
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• 2006

Due to the usefulness of the collaborative filtering, it has been widely used in both the research and commercial field. However, there are still some challenges for it to be more efficient, especially the scalability problem, the sparsity problem and the cold start problem. In this paper. we address these problems and provide a novel distributed approach based on agents collaboration for the problems. We have tried to solve the scalability problem by making each agent save its users ratings and broadcast them to the users friends so that only friends ratings and his own ratings are kept in an agents local database. To reduce quality degradation of recommendation caused by the lack of rating data, we introduce a method using friends opinions instead of real rating data when they are not available. We also suggest a collaborative filtering algorithm based on user profile to provide new users with recommendation service. Experiments show that our suggested approach is helpful to the new user problem as well as is more scalable than traditional centralized CF filtering systems and alleviate the sparsity problem.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.89-103
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• 2019

Recently, various approaches to recommendation systems have been studied in terms of the quality of recommendation system. A recommender system basically aims to provide personalized recommendations to users for specific items. Most of these systems always recommend the most relevant items of users or items. Traditionally, the evaluation of recommender system quality has focused on the various predictive accuracy metrics of these. However, recommender system must be not only accurate but also useful to users. User satisfaction with recommender systems as an evaluation criterion of recommender system is related not only to how accurately the system recommends but also to how much it supports the user's decision making. In particular, highly serendipitous recommendation would help a user to find a surprising and interesting item. Serendipity in this study is defined as a measure of the extent to which the recommended items are both attractive and surprising to the users. Therefore, this paper proposes an application of serendipity measure to recommender systems to evaluate the performance of recommender systems in terms of recommendation system quality. In this study we define relevant or attractive unexpectedness as serendipity measure for assessing recommendation systems. That is, serendipity measure is evaluated as the measure indicating how the recommender system can find unexpected and useful items for users. Our experimental results show that highly serendipitous recommendation such as item-based collaborative filtering method has better performance than the other recommendations, i.e. user-based collaborative filtering method in terms of recommendation system quality.

• Journal of the Korea Society for Simulation
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• v.15 no.3
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• pp.23-30
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• 2006

A recommender system for E-commerce site receives information from customers about which products they are interested in, and recommends products that are likely to fit their needs. In this paper, we investigate several methods for large-scale product purchase data for the purpose of producing useful recommendations to customers. We apply the traditional data mining techniques of cluster analysis and collaborative filtering(CF), and CF with reduction of product-dimensionality by use of latent semantic indexing(LSI). If reduced product-dimensionality obtained from LSI shows a similar latent trend of customers for buying products to that based on original customer-product purchase data, we expect less computational effort for obtaining the nearest-neighbor for target customer may improve the efficiency of recommendation performance. From simulation experiments on synthetic customer-product purchase data, CF-based method with reduction of product-dimensionality presents a better performance than the traditional CF methods with respect to the recall, precision and F1 measure. In general, the recommendation quality increases as the size of the neighborhood increases. However, our simulation results shows that, after a certain point, the improvement gain diminish. Also we find, as a number of products of recommendation increases, the precision becomes worse, but the improvement gain of recall is relatively small after a certain point. We consider these informations may be useful in applying recommender system.

• Journal of the Korea Society of Computer and Information
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• v.25 no.5
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• pp.99-106
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• 2020

The scalability problem inherent in collaborative filtering-based recommender systems has been an issue in related studies during past decades. Clustering is a well-known technique for handling this problem, but has not been actively studied due to its low performance. This paper adopts a clustering method to overcome the scalability problem, inherent drawback of collaborative filtering systems. Furthermore, in order to handle performance degradation caused by applying clustering into collaborative filtering, we take two strategies into account. First, we use fuzzy clustering and secondly, we propose and apply a similarity estimation method based on user preference for movie genres. The proposed method of this study is evaluated through experiments and compared with several previous relevant methods in terms of major performance metrics. Experimental results show that the proposed demonstrated superior performance in prediction and rank accuracies and comparable performance to the best method in our experiments in recommendation accuracy.

• Journal of the Korea Society of Computer and Information
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• v.28 no.5
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• pp.75-82
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• 2023

In collaborative filtering systems, the item rating prediction values calculated by the systems are very important for customer satisfaction with the recommendation list. In the time-aware system, predictions are calculated by reflecting the rating time of users, and in general, exponentially lower weights are assigned to past rating values. In this study, to find out whether the influence of rating time on the rating value varies according to various factors, the correlation between user rating value and rating time is investigated by the degree of user rating activity, the popularity of items, and item genres. As a result, using two types of public datasets, especially in the sparse dataset, significantly different correlation index values were obtained for each factor. Therefore, it is confirmed that the influence weight of the rating time on the rating prediction value should be set differently in consideration of the above-mentioned various factors as well as the density of the dataset.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.05a
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• pp.822-825
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• 2008

최근 모바일 기술의 발달로 모바일 네트워크에서 사용자들이 가지고 있는 정보를 활용하는 P2P 서비스들이 많이 연구되고 있다. 그 중에 협력적 필터링(Collaborative Filtering, CF)을 이용한 추천 서비스는 모바일 네트워크 상의 다른 사용자의 정보를 수집하여 개인화된 추천을 수행한다. 기존에 연구 된 CF 추천 시스템에서 메시지 전달을 위해 broadcasting 방식 사용되었다. broadcasting 방식은 각 모바일 기기 주위의 모든 기기로 사용자 정보를 전송함으로써 많은 트래픽을 유발시킨다. 본 논문에서는 슈퍼 피어 방식을 이용하여 메시지 전송 양을 줄여, CF를 이용한 추천 서비스를 보다 효율적으로 하고, 추천성능을 유지하게 하였다. 실험을 통해 본 논문에서 제시한 방식이 broadcasting 방식의 메시지 양을 53% 감소시켰음을 보였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2005.11a
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• pp.607-610
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• 2005

SCORM의 Content Repository는 Asset이나 컨텐츠의 Metadata를 가지고 컨텐츠나 Asset을 검색할 수 있도록 한다. 이런 Metadata 기반 검색은 아주 많은 컨텐츠를 대상으로 검색을 할 경우, 검색을 통한 컨텐츠 결과가 너무 많을 경우 결과 내에서 재검색을 하는데 많은 시간을 들일 수 있다는 단점이 있다. 본 논문에서는 검색 효율을 높이기 위한 방법으로 SCORM 기반 LMS에 협력 필터링 방법을 적용한 시스템을 제안하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.363-365
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• 2012

데이터의 규모 및 활용도, 그리고 사용자 접근성 측면에서 실세계 데이터에서 가장 중요한 이슈가 되는 것은 비디오 데이터이다. 장르나 등장인물, 배경 등이 매우 상이한 대량의 비디오 데이터들이 등장하고 있기 때문에, 통일된 사전지식을 이용한 비디오 데이터 분석이 매우 비현실적이 되어가고 있으며 사전지식을 활용하지 않는 비디오 분석기법의 중요성이 커지고 있다. 본 논문에서는 진화 파티를 필터링과 우점 이미지를 이용하여 비디오 데이터를 분절(Segmentation)하는 기법을 소개한다. 이미지 분절화 과정에서 해결해야 할 난점은 시점 변화 및 움직임 등에 의해 발생하는 사소한 변화가 컴퓨터 관점에서는 무시하기 어려운 큰 변화로 해석될 수 있다는 점이다. 동일장면에서의 시점 변화와 같은 사소한 변화로 인하여 동일 세그먼트를 추정하지 못하는 어려움을 해결하기 위하여 우리는 이미지 일부를 표현하는 파티클의 개체군을 생성하여 협력적인 방식으로 개별 이미지 세그먼트를 표현하는 방법을 개발하였다. 또한 동일 인물의 움직임과 같은 변화에 대응할 수 있도록 진화 파티를 필터링 방법을 컬러 히스토그램 방법과 결합하여 추론 성능을 한층 개선하였다. 실제 TV 드라마에 대하여 수행된 인간 평가자의 분절 평가 결과와 비교하여 제안 방법의 성능을 확인하였다.