• Proceedings of the Korean Information Science Society Conference
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• 2001.10b
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• pp.109-111
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• 2001

기존의 협력적 필터링 기술을 이용한 사용자 선호도 예측 방법에서는 아이템에 대한 사용자의 선호도를 기반으로 이웃 선정 방법(Nearest-Neighborhood Method)을 사용하고, 피어슨 상관 계수에 의해 사용자의 유사도를 구하므로 아이템에 대한 내용을 반영하지 못할 뿐만 아니라 희박성 문제를 해결하지 못하였다. 본 논문에서는 기존의 사용자 선호도 예측 방법의 문제점을 보완하기 위하여 연관 사용자 군집과 베이지안 분류를 이음한 사용자 선호도 예측 방법을 제안한다. 제안한 방법에서는 협력적 필터링 시스템에서의 희박성(Sparsity)문제를 해결하기 위하여 ARHP 알고리즘을 사용하여 사용자를 장르별로 군집하며 새로운 사용자는 Naive Bayes 분류자에 의해 이들 장르 중 하나로 분류된다. 또한, 분류된 장르 내에 속한 사용자들과 새로운 사용자의 유사도출 구하기 위해 Naive Bayes 학습을 통해 사용자가 평가한 아이템에 추정치를 달리 부여한다. 추정치가 부여된 선호도를 기존의 피어슨 상관 관계에 적용할 경우 결측치(Missing Value)로 인한 예측의 오류를 적게 하여 예측의 정확도를 높일 수 있다. 제안된 방법의 성능을 평가하기 위해서 기존의 협력적 필터링 기술과 비교 평가하였다.

• Journal of KIISE:Software and Applications
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• v.32 no.7
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• pp.581-591
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• 2005

Collaborative filtering systems have problems involving sparsity and the provision of recommendations by making correlations between only two users' preferences. These systems recommend items based only on the preferences without taking in to account the contents of the items. As a result, the accuracy of recommendations depends on the data from user-rated items. When users rate items, it can be expected that not all users ran do so earnestly. This brings down the accuracy of recommendations. This paper proposes a collaborative recommendation method for extracting typical group preferences using user-item matrix optimization and user profiles in collaborative tittering systems. The method excludes unproven users by using entropy based on data from user-rated items and groups users into clusters after generating user profiles, and then extracts typical group preferences. The proposed method generates collaborative user profiles by using association word mining to reflect contents as well as preferences of items and groups users into clusters based on the profiles by using the vector space model and the K-means algorithm. To compensate for the shortcoming of providing recommendations using correlations between only two user preferences, the proposed method extracts typical preferences of groups using the entropy theory The typical preferences are extracted by combining user entropies with item preferences. The recommender system using typical group preferences solves the problem caused by recommendations based on preferences rated incorrectly by users and reduces time for retrieving the most similar users in groups.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.4
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• pp.513-521
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• 2019

The purpose of this research is to find out the preference of users on the characteristics of the subway sign blank. In detail, The purpose is to introduce the concept of designing various characteristics of the subway sign blank according to characteristics such as gender and age, etc. The methodology of this study is to investigate the preference of the characteristics of the characteristics of the subway sign blank space and analyze the preference of the whole group and the cluster group. A survey was conducted to investigate preferences. A cluster analysis was conducted to analyze the preferences. And a demographic and conjoint analysis was conducted for whole group and the cluster group. The attributes of the subway sign blank space for preference survey are as follow : top and bottom blank, side blank, border line blank, arrow thickness, 'station name' and 'line number' order. The results of the preference analysis are as follows. The importance of the attributes in the whole group is shown in the order of the border line blank, 'station name' and 'line number' blank, side blank, top and bottom blank, and arrow thickness. The cluster group is composed of 3 groups, 1 cluster is a woman who uses the subway almost every day, three to four times a week, and seems to prefer half the side blank. 2 crowd is the user who thinks that 60 or more subway signs are uncomfortable, and preferring the order of 'station name' + 'line number' order without border. The 3 clusters were men in their 20s and 30s, with a preference for 1/5 border line blank and thin arrow thickness. The conclusion is as follows. First, the characteristics of the subway sign blank must be designed consistently. However, it is necessary to consider various factors according to gender, age, and frequency of subway use for specific regions or routes. Secondly, It has been shown that, depending on the specific area or route, it is possible to design two or more types of design, not one type of standardized marking of the characteristics of the subway sign blank.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.2
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• pp.277-284
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• 2017

In a flood of information, most users will want to get a proper recommendation. If a recommender system fails to give appropriate contents, then quality of experience (QoE) will be drastically decreased. In this paper, we propose a recommender system based on the intra-cluster users' item preference for improving recommendation accuracy indices such as precision, recall, and F1 score. To this end, first, users are divided into several clusters based on the actual rating data and Pearson correlation coefficient (PCC). Afterwards, we give each item an advantage/disadvantage according to the preference tendency by users within the same cluster. Specifically, an item will be received an advantage/disadvantage when the item which has been averagely rated by other users within the same cluster is above/below a predefined threshold. The proposed algorithm shows a statistically significant performance improvement over the item-based collaborative filtering algorithm with no clustering in terms of recommendation accuracy indices such as precision, recall, and F1 score.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.5
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• pp.587-595
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• 2017

The study examinesthe hindrance factors including various laws and institutions with regard to the Unbundled Parking System (UPS) that has recently attracted the greatest attention as a way to manage traffic demand for sustainable development, efficient utilization of parking spaces, and to overcome the unstable housing market.The direction of improvement of related laws and institutions is also suggested. Its usage is proven by countries such as the United States of America and France, as they have already implemented this institution. To lay the foundation for the introduction of UPS of our own country, a survey on the preference for UPS was conducted. The survey equally divided 300 respondents into three clusters based on the sales price of apartments in Seoul. The analyses revealed that all three clusters have similar preferences (cluster 1: 68%, cluster 2: 62%, cluster 3: 65%) on UPS, and younger groups seem to answer in the affirmative more than the other age groups no matter what cluster they belong to. In conclusion, the results on the estimation of selection probability on the preference of unbundled parking system and sales discount rate are as follow. The groups of non-vehicle users have higher preference on UPS. When the discount rate is 14%, 69%, 77% and 62%ofrespondents would choose unbundled parking system for clusters 1, 2 and 3, respectively (￦6,370,000/PY,￦3,930,000/PY and ￦2,270,000/PY reduce when applying avg. sales price, respectively).

• The Korean Journal of Applied Statistics
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• v.35 no.2
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• pp.265-283
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• 2022

MovieLens data consists of recorded movie evaluations that was often used to measure the evaluation score in the recommendation system research field. In this paper, we provide additional information obtained by clustering user-specific genre preference information through movie evaluation data and movie genre data. Because the number of movie ratings per user is very low compared to the total number of movies, the missing rate in this data is very high. For this reason, there are limitations in applying the existing clustering methods. In this paper, we propose a convex clustering-based method using the pairwise fused penalty motivated by the analysis of MovieLens data. In particular, the proposed clustering method execute missing imputation, and at the same time uses movie evaluation and genre weights for each movie to cluster genre preference information possessed by each individual. We compute the proposed optimization using alternating direction method of multipliers algorithm. It is shown that the proposed clustering method is less sensitive to noise and outliers than the existing method through simulation and MovieLens data application.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.287-296
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• 2003

There has been much research focused on collaborative filtering technique in Recommender System. However, these studies have shown the First-Rater Problem and the Sparsity Problem. The main purpose of this Paper is to solve these Problems. In this Paper, we suggest the user's predicting preference method using Bayesian estimated value and the associative user clustering for the recalculation of preference. In addition to this method, to complement a shortcoming, which doesn't regard the attribution of item, we use Representative Attribute-Neighborhood method that is used for the prediction when we find the similar neighborhood through extracting the representative attribution, which most affect the preference. We improved the efficiency by using the associative user's clustering analysis in order to calculate the preference of specific item within the cluster item vector to the collaborative filtering algorithm. Besides, for the problem of the Sparsity and First-Rater, through using Association Rule Hypergraph Partitioning algorithm associative users are clustered according to the genre. New users are classified into one of these genres by Naive Bayes classifier. In addition, in order to get the similarity value between users belonged to the classified genre and new users, and this paper allows the different estimated value to item which user evaluated through Naive Bayes learning. As applying the preference granted the estimated value to Pearson correlation coefficient, it can make the higher accuracy because the errors that cause the missing value come less. We evaluate our method on a large collaborative filtering database of user rating and it significantly outperforms previous proposed method.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10b
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• pp.169-173
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• 2006

유비쿼터스 상거래에서 사용자가 정보를 효율적으로 이용할 수 있도록 제어하고 필터링하는 일을 도와주는 개인화된 추천 시스템이 등장하였다. 더 나아가서는 사용자가 원하는 아이템을 예측하고 추천해주며, 이를 위해 협력적 필터링 기술을 적용하고 있다. 이는 사용자의 성향에 맞는 아이템을 예측하고 추천하기 위하여 비슷한 선호도를 가지는 사용자들 간의 유사도 가중치를 계산한다. 본 논문에서는 속성정보에 대한 사용자의 선호도를 고려하지 않은 문제점을 개선하기 위해서 속성정보를 이용한 연관 사용자의 선호도를 협력적 필터링 기술에 반영함으로써 추천의 정확도를 높이고자 한다. 그리고 협력적 필터링의 {연관 사용자-아이템} 행렬에서 사용자들 간의 연관 관계를 유지하면서 차원 수를 감소시키기 위해 ARHP 알고리즘을 이용하여 연관 사용자 군집을 한다. 제안된 방법의 성능 평가를 하기 위해 사용자가 아이템에 대해서 평가한 MovieLens 데이터 집합을 대상으로 평가되었으며, 기존의 Nearest Neighbor Model과 K-Means 군집보다 그 성능이 우수함을 보인다.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04b
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• pp.247-249
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• 2002

협력적 여과(collaborative filtering) 방법을 사용하는 추천 시스템에서 예측 정확도를 높이는 방법들 중 하나는 군집화(clustering)방법이 있다. 군집화 방법은 선호도가 유사한 사용자들을 미리 같은 군집으로 만들고, 군집 내에 속한 사용자들을 이웃으로 선정하여 예측을 수행하기 때문에 군집화의 결과가 예측의 정확도에 직접적인 영향을 주게 된다. 본 연구에서는 군집화 결과의 향상을 위해 데이터를 전 처리하는 두 가지 방법과 군집화의 특성을 이용한 새로운 예측식을 제안하고, 기존 연구 방법과의 비교 실험을 통해 실험결과를 분석한다.

• 한국디지털정책학회:학술대회논문집
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• 2005.11a
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• pp.163-173
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• 2005

금융기관들은 개인들의 다양한 욕구를 충족시키기 위해서 경쟁적으로 수많은 금융상품을 개발하여 판매하기 시작하였으며, 이러한 상품들은 나름대로 독특한 특성을 지니게 되어, 개인들은 이에 대한 수많은 정보를 수집하고 분석할 필요성을 가지게 되었다. 금융상품정보의 정보원 선택 선호요인으로는 정보원 자체의 특정 이외에도 개인이 가지는 성격적 특징과 환경적 요인들을 들 수 있다. 따라서 개인의 성격유형에 따라 선호하는 정보원의 유사점과 차이점을 알아보았다. 본 연구에서는 금융상품정보를 취득하는 정보원에 대한 선호요인인 개인의 성격유형을 파악하기 위하여 MBTI(Myers Briggs Type Indicator)를 근거로 작성된 성격특성평정척도(Personality Trait Ration Scales: PTRS)를 이용하여 파악된 성격유형들이 금융상품 정보원 선택에 미치는 영향을 분석하였다. 성격유형변수를 4개의 군집으로 집단화하고 성격요인과 차이분석을 실시한 결과 감정 직관적인 성격이 강한 주관적 감정형, 외향 직관적인 성격이 강한 사교적활동형, 내향 사고적인 성격이 강한 수동적개인형, 인식 판단적인 성격이 강한 합리적이성형의 4개 군집으로 나누어졌다. 8개의 성격요인 중 감각을 제외한 7개 성격요인 모두가 p<. 05에서 4개의 성격유형군집과 유의적인 차이가 있는 것으로 나타났다. 사교적활동형은 여러 정보원 중 'TV'를, 합리적이성형은 '잡지', '금융상품팜플렛', '재테크서적'을, 주관적감정형은 '은행창구직원', '친구나친지'를 선호하였다. 그러나 수동적개인형은 어떠한 금융상품정보원도 선호하지 않았다. 특히, 합리적이성형은 전문금융정보를 원하는 것으로 나타났고, 주관적감정형은 인간적인 면을 더 선호하는 것으로 나타났다. 본 연구가 가지는 의의는 각 성격유형별로 선호하는 금융상품정보원의 차이를 분석함으로써 개인의 정보욕구를 보다 더 만족시킬 수 있는 하나의 요인으로 성격요인과 정보원의 차이에 관한 정보를 제공하며, 금융정보를 제공하는 주체들에게 각 정보원에 적합하도록 정보의 성격에 관한 특성요인과 고객선호정보원을 살펴볼 수 있는 정보취득방안에 대한 연구의 필요성을 제시하는데 있다.