• Annual Conference on Human and Language Technology
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• 2013.10a
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• pp.64-69
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• 2013

스마트폰의 대중화로 인하여 앱 마켓 시장이 급속도로 성장하였다. 이로 인하여 하루에도 수십개의 새로운 앱들이 출시되고 있다. 이러한 앱 마켓 시장의 급격한 성장으로 인해 사용자들은 자신이 흥미를 가질만한 앱들을 선택하는데 큰 어려움을 겪고 있어 앱 추천 방법에 대한 연구에 많은 관심이 집중되고 있다. 기존 연구에서 협력 필터링 기반의 추천 방법들을 제안하였으나 이는 콜드 스타트 문제를 지니고 있다. 이와는 달리 컨텐츠 기반 필터링 방식은 콜드 스타트 문제를 효율적으로 해소할 수 있는 방법이지만 앱설명에는 광고, 공지사항등 실질적으로 앱의 특징과는 무관한 노이즈들이 다수 존재하고 이들은 앱 사이의 유사관계를 파악하는데 방해가 된다. 본 논문에서는 이런 문제를 해결하기 위하여 앱 설명에서 노이즈에 해당하는 설명들을 자동으로 제거할 수 있는 모델을 제안한다. 제안하는 모델은 모바일 앱 설명을 구성하고 있는 각 문단을 LDA로 학습된 토픽들의 비율로 나타내고 이들을 분류문제에서 우수한 성능을 보이는 SVM을 이용하여 분류한다. 실험 결과에 따르면 본 논문에서 제안한 방법은 기존에 문서 분류에 많이 사용되는 Bag-of-Word 표현법에 기반한 문서 표현 방식보다 더 나은 분류 성능을 보였다.

• The Journal of Society for e-Business Studies
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• v.10 no.2
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• pp.109-132
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• 2005

With the flood of multimedia contents over the digital TV channels, the internet, and etc., users sometimes have a difficulty in finding their preferred contents, spend heavy surfing time to find them, and are even very likely to miss them while searching. In this paper we suggests two-step clustering technique using time schema on how the system can recommend the user's preferred contents based on the collaborative filtering that has been proved to be successful when new users appeared. This method maps and recommends users' profile according to the gender and age at the first step, and then recommends a probabilistic item clustering customers who choose the same item at the same time based on time schema at the second stage. In addition, this has improved the accuracy of predictions in recommendation and the efficiency in time calculation by reflecting feedbacks of the result of the recommender engine and dynamically update customers' preference.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.203-210
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• 2020

Memory-based collaborative filtering is one of the representative types of the recommender system, but it suffers from the inherent problem of data sparsity. Although many works have been devoted to solving this problem, there is still a request for more systematic approaches to the problem. This study exploits distribution of user ratings given to items for computing similarity. All user ratings are utilized in the proposed method, compared to previous ones which use ratings for only common items between users. Moreover, for similarity computation, it takes a global view of ratings for items by reflecting other users' ratings for that item. Performance is evaluated through experiments and compared to that of other relevant methods. The results reveal that the proposed demonstrates superior performance in prediction and rank accuracies. This improvement in prediction accuracy is as high as 2.6 times more than that achieved by the state-of-the-art method over the traditional similarity measures.

• Journal of KIISE:Software and Applications
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• v.31 no.4
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• pp.467-477
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• 2004

While recommender systems were used by a few E-commerce sites former days, they are now becoming serious business tools that are re-shaping the world of I-commerce. And collaborative filtering has been a very successful recommendation technique in both research and practice. But there are two problems in personalized recommender systems, it is First-Rating problem and Sparsity problem. In this paper, we solve these problems using the associative relation clustering and “Lift” of association rules. We produce “Lift” between items using user's rating data. And we apply Threshold by -cut to the association between items. To make an efficiency of associative relation cluster higher, we use not only the existing Hypergraph Clique Clustering algorithm but also the suggested Split Cluster method. If the cluster is completed, we calculate a similarity iten in each inner cluster. And the index is saved in the database for the fast access. We apply the creating index to predict the preference for new items. To estimate the Performance, the suggested method is compared with existing collaborative filtering techniques. As a result, the proposed method is efficient for improving the accuracy of prediction through solving problems of existing collaborative filtering techniques.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2010.07a
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• pp.156-159
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• 2010

최근 스마트 폰은 사용의 편리성과 용도의 다양성 때문에 크게 확산 되고 있다. 또 스마트폰의 기능과 용도를 확장 시켜주기 위한 어플리케이션 역시 매일 수십 개씩 쏟아져 나오고 있다. 이미 나와 있는 수 만개의 어플리케이션과, 매일 업데이트 되는 어플리케이션들 사이에서 선호 어플리케이션을 찾기는 쉽지 않은 일이다. 이에 본 연구에선 협력 필터링 기반의 사용자 맞춤형 모바일 어플리케이션 추천시스템을 설계 및 제안한다. 사용자가 선호하는 어플리케이션을 추천 받을 수 있도록 함으로써 사용자에게 선호 정보를 보여 줄 수 있을 뿐만 아니라, 어플리케이션의 구매를 유도하여 새로운 가치 창출과 양방향성 마켓플레이스에 일조 할 수 있을 것이다.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.237-240
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• 2008

인터넷은 방대한 정보의 집합체이다. 사용자들은 웹에서 자신이 원하는 정보를 검색하여 사용하고 있다. 하지만 웹은 워낙 방대한 정보를 보유하고 있고 사용자가 원하는 정보가 다양해질수록 이러한 정보를 찾는 것은 어려워질 수 있다. 많은 유저들이 서로 다른 기호를 가지고 있는 만큼, 사용자에 따라 다른 형태의 정보를 제공하는 것이 필요하다. 이러한 형태의 서비스를 제공하기 위해서는 다양한 프로그램들이 상호협력하는 것이 필요하다. 본 논문은 데이터베이스를 활용한 멀티 에이전트 시스템을 통하여 사용자가 원하는 정보를 쉽게 관리하고 찾는 것에 목적을 둔다.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.163-170
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• 2023

The user-based collaborative filtering technique, one of the implementation methods of the recommendation system, recommends the preferred items of neighboring users based on the calculations of neighboring users with similar rating histories. However, it fundamentally has a data scarcity problem in which the quality of recommendations is significantly reduced when there is little common rating history. To solve this problem, many existing studies have proposed various methods of combining Jaccard index with a similarity measure. In this study, we introduce a time-aware concept to Jaccard index and propose a method of weighting common items with different weights depending on the rating time. As a result of conducting experiments using various performance metrics and time intervals, it is confirmed that the proposed method showed the best performance compared to the original Jaccard index at most metrics, and that the optimal time interval differs depending on the type of performance metric.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.146-150
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• 2007

As the internet has become a mainstream information tool, searching answers has become crucial as well. The collaborative filtering estimates and recommends items based upon the similar preference. However, because it refers to partial users information who have the similar preference, it tends to ignore the rest. In this paper, we propose the apparel coordination based on human sensibility ergonomics using the female students preference. This proposed method calculates evaluation values using fitness function based genetic algorithm, and gathers users through a-cut. Finally, the collaborative filtering recommends apparel coordination. To estimate the performance, the suggested method is compared with FAIMS-I, FAIMS-II in the questionnaire dataset.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.413-416
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• 2014

Recently, demand for various leisure sports gradually increases, as people's sense of values changes into leisure-centered one according to the change of given social circumstance and the change of customer needs all over the world. The actual condition is that an interest and participation rate especially in water leports during the summer increases. And needs for various hull design of standardized boat for water leisure increase. Therefore, this paper is intended to develop a recommendation system to design a boat for water leisure by using the collaborative filtering technique in order to make it possible to actively cope with the change of various customer needs for hull design. To this end, emotion relating to kayak design was selected through consumer survey, and emotion was derived by factor analysis and assessment, and then a kayak design layout in the aspect of customer's emotional preference was presented. Besides, an analysis was made according to the elements such as hull, body, and propulsion system of kayak in order to select emotional words according to the kayak design reflecting user's preference, and then a boat model for water leisure in conformance with user's preference was presented.

• Journal of the Korea Society of Computer and Information
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• v.26 no.5
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• pp.47-53
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• 2021

Sparse ratings data hinder reliable similarity computation between users, which degrades the performance of memory-based collaborative filtering techniques for recommender systems. Many works in the literature have been developed for solving this data sparsity problem, where the most simple and representative ones are the methods of utilizing Jaccard index. This index reflects the number of commonly rated items between two users and is mostly integrated into traditional similarity measures to compute similarity more accurately between the users. However, such integration is very straightforward with no consideration of the degree of data sparsity. This study suggests a novel idea of applying different similarity measures depending on the numeric value of Jaccard index between two users. Performance experiments are conducted to obtain optimal values of the parameters used by the proposed method and evaluate it in comparison with other relevant methods. As a result, the proposed demonstrates the best and comparable performance in prediction and recommendation accuracies.