• 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.

• The Journal of the Korea Contents Association
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• v.11 no.7
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• pp.1-8
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• 2011

In modern society, the amount of information has been significantly increased according to the development of Internet and IT convergence technology and that leads to develop information obtaining and searching technologies from lots of data. Although the system integration for medicare has been largely established and that accumulates large amounts of information, there is a lack of providing and supporting information for nursing activities using such established database. In particular, the judgement for the intervention of pains depends on the experience of individual nurses and that leads to make subjective decisions in usual. In this paper, a pain nursing supporting method that uses the existing medical data and performs collaborative filtering is proposed. The proposed collaborative filtering is a method that extracts some items, which represent a high relativeness level, based on similar preferences. A preference estimation method using a user based collaborative filtering method calculates user similarities through Pearson correlation coefficients in which a neighbor selection method is used based on the user preference.

• Science of Emotion and Sensibility
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• v.9 no.spc3
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• pp.179-185
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• 2006

This study examines and compares various recommendation techniques which have been used successfully in other fields and seeks for opportunity to improve design personalization service more effectively. Throughout the literature study, several major recommendation techniques were identified, namely 'contents-based filtering', 'collaborative filtering', and 'demographic filtering'. In order for finding out relative advantages and disadvantages, a case study was carried out by applying different techniques. The result showed that in general, demographic filtering was evaluated least efficient among the techniques. Content-based filtering showed the best efficiency among them. Another significant finding was that the collaborative filtering had a better efficiency as the number of test subjects is increased. In conclusion, we suggest that design recommendation services can be improved by applying contents-based or collaborative filtering for better efficiency of recommendation. And, if the number of test subjects is large enough, it may be possible to remarkably improve the efficiency of design recommendation services by using collaborative filtering.

• 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 KIISE:Computing Practices and Letters
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• v.9 no.5
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• pp.541-550
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• 2003

It is important for the strategy of product sales to investigate the consumer's sensitivity and preference degree in the environment that the process of material development has been changed focusing on the consumer renter. In the present study, we propose the Fashion Design Recommender System (FDRS) of textile design applying collaborative filtering personalization technique as one of methods in the material development centered on consumer's sensibility and preferences. In collaborative filtering personalization technique based on textile, Pearson Correlation Coefficient is used to calculate similarity weights between users. We build the database founded on the sensibility adjective to develop textile designs by extracting the representative sensibility adjective from users' sensibility and preferences about textile designs. FDRS recommends textile designs to a consumer who has a similar propensity about textile. Ultimately, this paper sugeests empirical applications to verify the adequacy and the validity on this system with the development of Fashion Design Recommender System (FDRS)

• 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.

• The Journal of Korean Association of Computer Education
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• v.14 no.5
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• pp.97-104
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• 2011

Collaborative filtering is a methodology to recommend websites by obtaining data and opinions from the other users with similar tastes. During the past few years, this method has been used in various fields such as books, food, and movies in e-commerce systems. This study addresses the computation of similarity between users to determine items to be recommended in collaborative filtering systems. Previous studies measured similarity between users by treating each user's ratings independently without considering the distribution of the user's ratings. In contrast, this study measures similarity by utilizing position and rank information of each rating in the range of the user's ratings. The result of the experiments on the real datasets demonstrated that the proposed method improves the mean absolute error significantly, compared to the previous methods, especially when the predetermined range of ratings is large.

• The Journal of the Korea Contents Association
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• v.7 no.4
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• pp.66-73
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• 2007

Internet is a part of our common life and tremendous information is cumulated. In these trends, the personalization becomes a very important technology which could find exact information to present users. Previous personalized services use content based filtering which is able to recommend by analyzing the content and collaborative filtering which is able to recommend contents according to preference of users group. But, collaborative filtering needs the evaluation of some amount of data. Also, It cannot reflect all data of users because it recommends items based on data of some users who have similar inclination. Therefore, we need a new recommendation method which can recommend prefer items without preference data of users. In this paper, we proposed the apparel coordination system using personalized preference on the semantic web. This paper provides the results which this system can reduce the searching time and advance the customer satisfaction measurement according to user's feedback to system.

• 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.

• The KIPS Transactions:PartB
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• v.8B no.2
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• pp.144-154
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• 2001

협력적 여과 시스템은 사용자가 검색하고 읽었던 웹문서를 기반으로 사용자 군집을 생성하여 웹문서의 정확한 추천을 가능하게 한다. 이러한 목적으로 설계된 다양한 알고리즘이 있으나 속도가 느리거나 정확도가 낮다는 등의 단점이 있다. 본 논문에서는 이러한 단점을 보완하기 위하여 협력적 여과 시스템을 위한 효과적인 사용자 군집 알고리즘인 CUG알고리즘은 사용자 군집을 생성하기 위해 Apriori 알고리즘, Native Bayes 알고리즘을 이용한다. Apriori 알고리즘은 연관 단어 지식 베이스를 구축하고, Native Bayes 알고리즘은 구축된 연관 단어 지식 베이스에 가중치를 추가하며, 사용자가 검색하여 읽은 웹문서를 클래스별로 분류한다. CUG 알고리즘은 분류된 웹문서를 기반으로 하여 사용자 군집을 만든다. 이러한 방법으로 설계된 CUG 알고리즘은 사용자들이 사용할 문서를 미리 검색하여 저장함에 의해 정보검색의 효율성을 향상시키는데 사용될 수 있다. 본 논문에서 설계한 CUG 알고리즘의 선능을 평가하기 위하여 기존의 K-means 방법과 Gibbs샘플링 방법에 의한 군집과 비교한다.