• Proceedings of the Korea Information Processing Society Conference
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• 2016.10a
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• pp.393-396
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• 2016

정보화 시대에 들어오며 수많은 정보들의 폭발적인 증가로 인해 사용자들은 원하는 정보를 빠른 시간에 얻는 것이 어려워졌다. 그중 영화는 수없이 많은 정보를 누적해왔고 개인에 따라 선호하는 영화가 서로 다르기 때문에 각 개인에 맞는 영화를 찾는 것은 쉽지 않다. 본 논문에서는 협업 필터링과 개인의 감정을 이용하고 AWS(Amazon Web Service)를 통한 클라우드 컴퓨팅 시스템을 사용하여 각 개인에 더 적합한 영화 추천 시스템을 제안 한다.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06b
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• pp.216-219
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• 2011

인터넷 기술이 빠르게 발전하고 변화하여 현재는 많은 수의 컨텐츠와 프로그램 채널이 IP 네트워크를 통해 제공되면서 컨텐츠 서비스 사업자들은 좀 더 향상된 추천시스템이 필요하게 되었다. 그리고 사용자 참여중심의 인터넷 환경인 Web 2.0 시대가 도래하면서 사용자가 직접 생성한 정보들을 활용하는 다양한 연구가 진행되고 있다. 본 논문에서는 타겟 아이템에 대해 인터넷 상에 수많은 사용자들이 생성한 정보들을 ConceptNet을 활용하여 감정벡터를 추출하고 장르별로 분류하는 방법을 결합한 새로운 형태의 영화 추천시스템을 제안한다. 공개용 영화 데이터인 MovieLens 데이터 셋을 이용하여 실험하였고 성능평가는 RMSE 방법과 다양한 추천평가방법으로 기존 협업 필터링 추천시스템과 비교하였으며 실험 결과 기존방식보다 향상된 성능을 보였다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.1
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• pp.101-107
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• 2014

This paper presents an efficient distributed recommendation system using clustering collaborative filtering algorithm in distributed computing environments. The system was built based on Hadoop distributed computing platform, where distributed Min-hash clustering algorithm is combined with user based collaborative filtering algorithm to optimize recommendation performance. Experiments using Movie Lens benchmark data show that the proposed system can reduce the execution time for recommendation compare to sequential system.

• Proceedings of the Korean Society of Computer Information Conference
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• 2011.06a
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• pp.171-172
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• 2011

협업적 여과 시스템은 사용자에 대한 클러스터링을 구축한 후, 구축된 클러스터를 기반으로 사용자에게 영화를 추천한다. 하지만 사용자 클러스터링 구축에 많은 시간이 소요되고, 사용자가 평가한 영화가 피드백이 되었을 경우 재구축이 쉽지 않다. 본 논문에서는 사용자 클러스터링의 재구축을 용이하게 하기 위해 빈발패턴 네트워크를 이용하여 클러스터링을 구축하고, 이를 협업적 여과 시스템에 적용하여 영화를 추천한다. 구축된 클러스터를 통해 사용자 클러스터를 재구축시 소요되는 시간 비용을 줄이면서, 전통적인 협업적 여과 시스템과 유사한 성능의 추천이 가능하게 되었다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.67-72
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• 2021

Association rule mining is a method of showing the relationship between patterns hidden in several tables. These days, granulation logic is used to add more detailed meaning to association rule mining. In addition, unlike the existing system that recommends using existing data, the granulation related rules can also recommend new subscribers or new products. Therefore, determining the qualitative size of the granulation of the association rule determines the performance of the recommendation system. In this paper, we propose a granulation method for subscribers and movie data using fuzzy logic and Shannon entropy concepts in order to understand the relationship to the movie evaluated by the viewers. The research is composed of two stages: 1) Identifying the size of granulation of data, which plays a decisive role in the implications of the association rules between viewers and movies; 2) Mining the association rules between viewers and movies using these granulations. We preprocessed Netflix's MovieLens data. The results of meanings of association rules and accuracy of recommendation are suggested with managerial implications in conclusion section.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.19-42
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• 2012

Consumer consumption patterns are shifting rapidly as buyers migrate from offline markets to e-commerce routes, such as shopping channels on TV and internet shopping malls. In the offline markets consumers go shopping, see the shopping items, and choose from them. Recently consumers tend towards buying at shopping sites free from time and place. However, as e-commerce markets continue to expand, customers are complaining that it is becoming a bigger hassle to shop online. In the online shopping, shoppers have very limited information on the products. The delivered products can be different from what they have wanted. This case results to purchase cancellation. Because these things happen frequently, they are likely to refer to the consumer reviews and companies should be concerned about consumer's voice. E-commerce is a very important marketing tool for suppliers. It can recommend products to customers and connect them directly with suppliers with just a click of a button. The recommender system is being studied in various ways. Some of the more prominent ones include recommendation based on best-seller and demographics, contents filtering, and collaborative filtering. However, these systems all share two weaknesses : they cannot recommend products to consumers on a personal level, and they cannot recommend products to new consumers with no buying history. To fix these problems, we can use the information which has been collected from the questionnaires about their demographics and preference ratings. But, consumers feel these questionnaires are a burden and are unlikely to provide correct information. This study investigates combining collaborative filtering with the centrality of social network analysis. This centrality measure provides the information to infer the preference of new consumers from the shopping history of existing and previous ones. While the past researches had focused on the existing consumers with similar shopping patterns, this study tried to improve the accuracy of recommendation with all shopping information, which included not only similar shopping patterns but also dissimilar ones. Data used in this study, Movie Lens' data, was made by Group Lens research Project Team at University of Minnesota to recommend movies with a collaborative filtering technique. This data was built from the questionnaires of 943 respondents which gave the information on the preference ratings on 1,684 movies. Total data of 100,000 was organized by time, with initial data of 50,000 being existing customers and the latter 50,000 being new customers. The proposed recommender system consists of three systems : [+] group recommender system, [-] group recommender system, and integrated recommender system. [+] group recommender system looks at customers with similar buying patterns as 'neighbors', whereas [-] group recommender system looks at customers with opposite buying patterns as 'contraries'. Integrated recommender system uses both of the aforementioned recommender systems to recommend movies that both recommender systems pick. The study of three systems allows us to find the most suitable recommender system that will optimize accuracy and customer satisfaction. Our analysis showed that integrated recommender system is the best solution among the three systems studied, followed by [-] group recommended system and [+] group recommender system. This result conforms to the intuition that the accuracy of recommendation can be improved using all the relevant information. We provided contour maps and graphs to easily compare the accuracy of each recommender system. Although we saw improvement on accuracy with the integrated recommender system, we must remember that this research is based on static data with no live customers. In other words, consumers did not see the movies actually recommended from the system. Also, this recommendation system may not work well with products other than movies. Thus, it is important to note that recommendation systems need particular calibration for specific product/customer types.

• Journal of Intelligence and Information Systems
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• v.13 no.4
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• pp.65-78
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• 2007

This paper proposes a new method of weighted template matching for machine-printed numeral recognition. The proposed weighted template matching, which emphasizes the feature of a pattern using adaptive Hamming distance on local feature areas, improves the recognition rate while template matching processes an input image as one global feature. Template matching is vulnerable to random noises that generate ragged outlines of a pattern when it is binarized. This paper offers a method of chain code trimming in order to remove ragged outlines. The method corrects specific chain codes within the chain codes of the inner and the outer contour of a pattern. The experiment compares confusion matrices of both the template matching and the proposed weighted template matching with chain code trimming. The result shows that the proposed method improves fairly the recognition rate of the machine-printed numerals.

• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.201-207
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• 2022

In this paper, we propose a movie recommendation model that uses the users' ratings as well as their reviews. To understand the user's preference from multicriteria perspectives, the proposed model is designed to apply attribute-based sentiment analysis to the reviews. For doing this, it divides the reviews left by customers into multicriteria components according to its implicit attributes, and applies BERT-based sentiment analysis to each of them. After that, our model selectively combines the attributes that each user considers important to CF to generate recommendation results. To validate usefulness of the proposed model, we applied it to the real-world movie recommendation case. Experimental results showed that the accuracy of the proposed model was improved compared to the traditional CF. This study has academic and practical significance since it presents a new approach to select and use models in consideration of individual characteristics, and to derive various attributes from a review instead of evaluating each of them.

• Journal of Digital Convergence
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• v.18 no.12
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• pp.525-531
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• 2020

In today's IT environment where various pieces of information are distributed in large volumes, recommendation systems are in the spotlight capable of figuring out users' needs fast and helping them with their decisions. The current recommendation systems, however, have a couple of problems including that user preference may not be reflected on the systems right away according to their changing tastes or interests and that items with no relations to users' preference may be recommended, being induced by advertising. In an effort to solve these problems, this study set out to propose a Fuzzy-AHP-based movie recommendation system by applying the BRNN(Bidirectional Recurrent Neural Network) language model. Applied to this system was Fuzzy-AHP to reflect users' tastes or interests in clear and objective ways. In addition, the BRNN language model was adopted to analyze movie-related data collected in real time and predict movies preferred by users. The system was assessed for its performance with grid searches to examine the fitness of the learning model for the entire size of word sets. The results show that the learning model of the system recorded a mean cross-validation index of 97.9% according to the entire size of word sets, thus proving its fitness. The model recorded a RMSE of 0.66 and 0.805 against the movie ratings on Naver and LSTM model language model, respectively, demonstrating the system's superior performance in predicting movie ratings.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.460-462
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• 2003

본 논문에서는 모바일 환경에서. 사용자의 상황정보와 개인적 선호도 정보를 고려하여 사용자에게 적합한 영화를 선택. 추천하고 선택된 영화의 요약(Summary)을 서비스 할 수 있는 시스템을 제안한다. 제안된 시스템은 사용자가 이동하는 상황에 따라 변하는 위치, 시간 정보와 개인적 선호도인 영화장르 정보에 기반한 영화선택 서비스를 제공하고, 선택된 영화 콘텐츠의 요약을 MPEG-7 메타데이터로 기술하고, 이를 이용해 요약을 효과적으로 소모할 수 있게 한다. 제안된 시스템을 통해, 모바일 환경 기반 영화 선택 및 서비스 시스템(Mobile Environment-Based Movie Selection and Summary Service System)을 실현하고, 그 효용성을 입증하였다.