• Journal of KIISE:Software and Applications
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• v.31 no.12
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• pp.1672-1683
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• 2004

Recommender system is a kind of web intelligence techniques to make a daily information filtering for people. Researchers have developed collaborative recommenders (social recommenders), content-based recommenders, and some hybrid systems. In this paper, we introduce a new hybrid recommender method - ICHM where clustering techniques have been applied to the item-based collaborative filtering framework. It provides a way to integrate the content information into the collaborative filtering, which contributes to not only reducing the sparsity of data set but also solving the cold start problem. Extensive experiments have been conducted on MovieLense data to analyze the characteristics of our technique. The results show that our approach contributes to the improvement of prediction quality of the item-based collaborative filtering, especially for the cold start problem.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06c
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• pp.404-409
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• 2010

협력 필터링(Collaborative Filtering)은 상품추천, 영화추천 등에 사용되는 대표적인 방법으로서, 사용자들의 사용이력에 기반해서 유사도가 높은 항목들을 찾아낸다. 본 연구에서는 상용 IPTV 서비스에 협력 필터링을 적용했을 때 만들어지는 모델을 분석하여 어떤 요소들이 협력 필터링 모델의 생성에 영향을 끼치는지 분석했다. 이를 통해 IPTV 영역에 협력 필터링을 적용했을 때 영향을 끼치는 요소들과 다른 영역과는 다르게 고려해야 할 사항들을 알 수 있었다.

• The Journal of Korean Association of Computer Education
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• v.14 no.4
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• pp.63-70
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• 2011

Recommender systems, which predict and recommend items that may possibly draw users' interests, have been applied in various fields as e-commerce systems are widespread. Collaborative filtering, one of the major methodologies of recommender systems, recommends either items similar to those preferred by the user, or items preferred by the other similar user. Therefore, two problems determine its performance; one is correct estimation of similarity and the other is predicting the real rating of the recommended item. This study addresses the latter problem. Previous studies predict the real rating based on the mean of the ratings, but this study proposes a prediction based on the range of the ratings and investigates its performance through experiments. As a result, it is demonstrated that the proposed method improves the mean absolute error significantly, compared to the previous method.

• Journal of Digital Convergence
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• v.12 no.8
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• pp.329-335
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• 2014

Collaborative filtering has been popular in commercial recommender systems, as it successfully implements social behavior of customers by suggesting items that might fit to the interests of a user. So far, most common method to find proper items for recommendation is by searching for similar users and consulting their ratings. This paper suggests a new similarity measure for movie recommendation that is based on genre interest, instead of differences between ratings made by two users as in previous similarity measures. From extensive experiments, the proposed measure is proved to perform significantly better than classic similarity measures in terms of both prediction and recommendation qualities.

• The Journal of Korean Association of Computer Education
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• v.19 no.1
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• pp.111-118
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• 2016

Measuring similarity in collaborative filtering-based recommender systems greatly affects system performance. This is because items are recommended from other similar users. In order to overcome the biggest problem of traditional similarity measures, i.e., data sparsity problem, this study suggests a new similarity measure that is the optimal combination of previous similarity and the value reflecting the number of co-rated items. We conducted experiments with various conditions to evaluate performance of the proposed measure. As a result, the proposed measure yielded much better performance than previous ones in terms of prediction qualities, specifically the maximum of about 7% improvement over the traditional Pearson correlation and about 4% over the cosine similarity.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.363-366
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• 2005

Pure P2P 환경에서는 축적된 자료를 사용하지 않고 실시간 정보를 사용하여 소수의 서비스 항목만으로도 협력적 필터링을 제공할 수 있어야 한다 그러나 지역에서 수집된 소수의 서비스 항목만으로 협력적 필터링을 할 경우 추천 서비스의 질이 떨어지게 되므로, 사용자의 컨텍스트 정보를 이용하여 추천 서비스의 질을 높일 수 있는 방법이 연구되어야 한다. 하지만 사용자 컨텍스트 정보는 다량의 정보가 순간에 인식될 수 있기 때문에 확장성 문제(Scalability Problem)가 발생하고, 영역과 아이템에 따라 차별화된 서비스를 지원하기에는 한계성을 가지고 있다. 이러한 문제점을 해결하기 위하여 본 연구에서는 SOM을 이용하여 컨텍스트 정보를 서비스 영역별로 클러스터링(Clustering)하여, 사용자별로 분류함으로 확장성 문제를 해결하였다. 또한, 분류된 자료들 중 서비스 요구자와 비슷한 분류에 있는 사용자들의 컨텍스트 정보들을 정량화하여 협력적 필터링함으로 사용자에게 적합한 서비스를 지원할 수 있다.

• Journal of KIISE:Software and Applications
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• v.32 no.9
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• pp.901-911
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• 2005

With the development of e-commerce and the proliferation of easily accessible information, information filtering has become a popular technique to prune large information spaces so that users are directed toward those items that best meet their needs and preferences. While many collaborative filtering systems have succeeded in capturing the similarities among users or items based on ratings to provide good recommendations, there are still some challenges for them to be more efficient, especially the user bias problem, non-transitive association problem and cold start problem. Those three problems impede us to capture more accurate similarities among users or items. In this paper, we provide probabilistic model approaches for UCHM and ICHM which are suggested to solve the addressed problems in hopes of achieving better performance. In this probabilistic model, objects (users or items) are classified into groups and predictions are made for users considering the Gaussian distribution of user ratings. Experiments on a real-word data set illustrate that our proposed approach is comparable with others.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.2
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• pp.224-229
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• 2011

Collaborative filtering is used the most for recommendation systems because it can recommend potential items. However, when there are not many items to be evaluated, collaborative filtering can be subject to the influence of similarity or preference depending on the situation or the whim of the evaluator. In addition, by recommending items only on the basis of similarity with items that have been evaluated previously without relation to the present situation of the user, the recommendations become less accurate. In this paper, in order to solve the above problems, before starting the collaborative filtering procedure, we calculated similarity not by comparing all the values evaluated by users but rather by comparing only those users who were above the average in order to improve the accuracy of the recommendations. In addition, in the ceaselessly changing ubiquitous computing environment, it is not proper to recommend service information based only on the items evaluated by users. Therefore, we used methods of calculating similarity wherein the users' real time context information was used and a high weight was assigned to similar users. Such methods improved the recommendation accuracy by 16.2% on average.

• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.93-100
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• 2022

In the era of information overload on the Internet, the recommendation system, which is an indispensable function, is a service that recommends products that a user may prefer, and has been successfully provided in various commercial sites. Recently, studies to reflect the rating time of items to improve the performance of collaborative filtering, a representative recommendation technique, are active. The core idea of these studies is to generate the recommendation list by giving an exponentially lower weight to the items rated in the past. However, this has a disadvantage in that a time function is uniformly applied to all items without considering changes in users' preferences according to the characteristics of the items. In this study, we propose a time-aware collaborative filtering technique from a completely different point of view by developing a new similarity measure that integrates the change in similarity values between items over time into a weighted sum. As a result of the experiment, the prediction performance and recommendation performance of the proposed method were significantly superior to the existing representative time aware methods and traditional methods.

• The Journal of Korean Association of Computer Education
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• v.21 no.5
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• pp.61-68
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• 2018

Collaborative filtering is a fundamental technique implemented in many commercial recommender systems and provides a successful service to online users. This technique recommends items by referring to other users who have similar rating records to the current user. Hence, similarity measures critically affect the system performance. This study addresses problems of previous similarity measures and suggests a new similarity measure. The proposed measure reflects the subjectivity or vagueness of user ratings and the users' rating behavior by using fuzzy logic. We conduct experimental studies for performance evaluation, whose results show that the proposed measure demonstrates outstanding performance improvements in terms of prediction accuracy and recommendation accuracy.