• 한국컴퓨터정보학회논문지
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• 제24권9호
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• pp.77-82
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• 2019

Collaborative filtering is a popular technique for recommender systems and used in many practical commercial systems. Its basic principle is select similar neighbors of a current user and from their past preference information on items the system makes recommendations for the current user. One of the major problems inherent in this type of system is data sparsity of ratings. This is mainly caused from the underlying similarity measures which produce neighbors based on the ratings records. This paper handles this problem and suggests a new similarity measure. The proposed method takes users rating patterns into account for computing similarity, without just relying on the commonly rated items as in previous measures. Performance experiments of various existing measures are conducted and their performance is compared in terms of major performance metrics. As a result, the proposed measure reveals better or comparable achievements in all the metrics considered.

• Journal of applied mathematics & informatics
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• 제28권1_2호
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• pp.439-450
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• 2010

In this paper, we propose a new idea to evaluate the prediction accuracy of user's preference generated by memory-based collaborative filtering algorithm before prediction process in the recommender system. Our analysis results show the possibility of a pre-evaluation before the prediction process of users' preference of item's transaction on the web. Classification functions proposed in this study generate a user's rating pattern under certain conditions. In this research, we test whether classification functions select users who have lower prediction or higher prediction performance under collaborative filtering recommendation approach. The statistical test results will be based on the differences of the prediction accuracy of each user group which are classified by classification functions using the generative probability of specific rating. The characteristics of rating patterns of classified users will also be presented.

• 디지털콘텐츠학회 논문지
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• 제18권3호
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• pp.535-542
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• 2017

유비쿼터스 컴퓨팅은 사용자의 위치, 상태, 행동정보, 주변 상황 등의 컨텍스트를 인식할 수 있게 하였는데 이로 인해 사용자에게 필요한 서비스를 빠르고 정확하게 제공해 줄 수 있게 되었다. 이와 같은 개인화 추천 서비스는 사용자의 컨텍스트 정보를 인식하고 해석하는 추론기술이 필요한데 본 논문에서는 실생활과 가장 밀접한 음식점을 날씨, 시간, 요일, 위치의 모바일 컨텍스트 데이터를 기반으로 행동 패턴을 추론하여 추천하는 모델을 연구한다. 연구를 위해 자사에서 직접 서비스 하고 있는 사용자 평가 기반 음식점 추천 서비스의 장소와 사용자 생성 데이터를 활용하였고, 행동패턴을 추론하기 위해 나이브 베이즈 방정식을 사용했다. 그리고 선호도 예측 알고리즘을 활용하여 추천 장소를 선정하였다. 시스템으로 구현하여 평가 기반의 추천 방식보다 본 논문에서 제시한 연구의 우수성도 입증하였다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2008년도 하계종합학술대회
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• pp.641-642
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• 2008

In this paper, we propose a new approach for the implicit rating algorithm of finding user's intense and preference to the contents on the web. Although the explicit method dig out the user preference of specific contents based on the user's intervention, we propose the implicit method obtaining the user preference according to the user's behavioral patterns on the web implicitly and automatically without the user's intervention. The implementation results show that the proposed approach is highly valuable for supporting recommender systems in conjunction with the users lifestyle.

• 데이타베이스연구회지:데이타베이스연구
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• 제34권3호
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• pp.99-123
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• 2018

웹상에서의 기하급수적으로 증가하는 정보의 양으로 인해, 중요하고 가치 있는 데이터를 변별 해 내는 작업은 그 어느 때보다도 중요하다고 하겠다. 추천 시스템은 이러한 정보의 과 공급 문제를 해결하기 위한 가장 효과적인 방법 중 하나임에도 불구하고, 그 성능은 기존 방식들에서 크게 진전을 이루지 못하고 있는 것이 사실이다. 따라서 본 논문에서는 이 문제를 진전시키기 위해, 협업태그를 활용한 새로운 사용자 프로파일링 기법을 제안하고 사용자의 평가 및 태깅패턴을 분석, 그 활용 또한 모색한다. 본 논문에서 제안하는 기법의 검증을 위해, 해당 프로파일링 기법을 활용 한 혼합 영화 추천 시스템을 구현하고 실제 데이터를 사용하여 기존의 추천 방식 대비 그 경쟁력을 검증하였다. 그와 더불어, 민감도 분석을 통해 사용자의 태깅패턴과 평가패턴에 기반한 차별적인 추천 방식의 잠재적 가능성 또한 제안, 검증한다.

• 대한건축학회논문집:계획계
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• 제35권1호
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• pp.37-46
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• 2019

The energy of the building is influenced by the user 's activity due to the population, society, and economic characteristics of the building user. In order to obtain accurate energy information, the difference in the amount of energy consumption by the activities and characteristics of building users should be identified. The purpose of the study is to identify the difference in the amount of energy consumption by the user's activities in the same building, and to analyse the relationship between user's activities and demographic, social and economic characteristics. For research, energy simulation is performed based on actual user activity schedule. The results of the simulation were clustered by using K-Means clustering, a machine learning technique. As a result, four types of users were derived based on the amount of energy consumption. The more energy used in a cluster, the lower the user's income level and older. The longer a user's indoor activity times, the higher the energy use, and these activities relate to the user's characteristics. There is more than twice the difference between the group that uses the least energy consumption and the group that uses the most energy consumption.

• Journal of Information Processing Systems
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• 제9권3호
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• pp.435-460
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• 2013

Recommender systems are popular applications that help users to identify items that they could be interested in. A recent research area on recommender systems focuses on detecting several kinds of inconsistencies associated with the user preferences. However, the majority of previous works in this direction just process anomalies that are intentionally introduced by users. In contrast, this paper is centered on finding the way to remove non-malicious anomalies, specifically in collaborative filtering systems. A review of the state-of-the-art in this field shows that no previous work has been carried out for recommendation systems and general data mining scenarios, to exactly perform this preprocessing task. More specifically, in this paper we propose a method that is based on the extraction of knowledge from the dataset in the form of rating regularities (similar to frequent patterns), and their use in order to remove anomalous preferences provided by users. Experiments show that the application of the procedure as a preprocessing step improves the performance of a data-mining task associated with the recommendation and also effectively detects the anomalous preferences.

• 국제물리치료학회지
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• 제3권2호
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• pp.469-474
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• 2012

The purpose of this study is to analyze the correlation between the stature and the muscle performance ratings and the subjective discomfort rations at performing lower arm's pronation and supination according to change sin the height of working table for more efficient performance at designing a working table and performing a work. For the purpose, this study conducted an experiment targeting 40 people in their 20s, who were classified into 4 groups each group composing 10 people at intervals of 5cm from the standard stature of 166.5cm. The experiment measured the maximum isometric pronation and the supination muscular power, and at measuring the factors, the heights of working tables were set as 800mm, 850mm, and 900mm. From the measurement results, it was found that the stature and the maximum muscular power was correlated. That is, as the experiment groups's average stature is higher, the maximum muscular power was higher. For the correlation between the motion patterns(pronation and supination) and the maximum muscular power, it was seen that the maximum muscular power was higher at performing the pronation than the supination. In the correlation between motion patterns and the subjective discomfort ratings, it was seen that the subjective discomfort rating was higher at performing the supination than the pronation. For the correlation between height adjustment and the subjective discomfort ratings, as the height of working table was lower, the subject discomfort rating was lower. Therefore there was no difference in the maximum muscular power according to the height changes of working table, but it was found that as the working table was higher, the user felt more comfortable.

• 정보관리학회지
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• 제39권3호
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• pp.311-336
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• 2022

본 연구는 도서관 정보서비스 환경에서 도서 이용자의 도서추천에 영향을 미치는 선호요인을 파악하기 위해 전 세계 도서 이용자의 참여로 이루어지는 사회적 목록 서비스인 Goodreads 리뷰 데이터를 대상으로 내용분석하였다. 이용자 선호의 내용을 보다 세부적인 관점에서 파악하기 위해 샘플 선정 과정에서 평점 그룹별, 도서별, 이용자별 하위 데이터 집합을 구성하였으며, 다양한 토픽을 고루 반영하기 위해 리뷰 텍스트의 토픽모델링 결과에 기반하여 층화 샘플링을 수행하였다. 그 결과, '내용', '캐릭터', '글쓰기', '읽기', '작가', '스토리', '형식'의 7개 범주에 속하는 총 90개 선호요인 관련 개념을 식별하는 한편, 평점에 따라 드러나는 일반적인 선호요인은 물론 호불호가 분명한 도서와 이용자에서 드러나는 선호요인의 양상을 파악하였다. 본 연구의 결과는 이용자 선호요인의 구체적 양상을 파악하여 향후 추천시스템 등에서 보다 정교한 추천에 기여할 수 있을 것으로 보인다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권2호
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• pp.538-561
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• 2020

Recommender Systems (RecSys) have a major role in e-commerce for recommending products, which they may like for every user and thus improve their business aspects. Although many types of RecSyss are there in the research field, the state of the art RecSys has focused on finding the user similarity based on sequence (e.g. purchase history, movie-watching history) analyzing and prediction techniques like Recurrent Neural Network in Deep learning. That is RecSys has considered as a sequence prediction problem. However, evaluation of similarities among the customers is challenging while considering temporal aspects, context and multi-component ratings of the item-records in the customer sequences. For addressing this issue, we are proposing a Deep Learning based model which learns customer similarity directly from the sequence to sequence similarity as well as item to item similarity by considering all features of the item, contexts, and rating components using Dynamic Temporal Warping(DTW) distance measure for dynamic temporal matching and 2D-GRU (Two Dimensional-Gated Recurrent Unit) architecture. This will overcome the limitation of non-linearity in the time dimension while measuring the similarity, and the find patterns more accurately and speedily from temporal and spatial contexts. Experiment on the real world movie data set LDOS-CoMoDa demonstrates the efficacy and promising utility of the proposed personalized RecSys architecture.