신상품 추천을 위한 사회연결망분석의 활용

조윤호(Yoonho Cho),방정혜(Jounghae Bang) 한국지능정보시스템학회 2009 지능정보연구 Vol.15 No.4

Collaborative Filtering is one of the most used recommender systems. However, basically it cannot be used to recommend new products to customers because it finds products only based on the purchasing history of each customer. In order to cope with this shortcoming, many researchers have proposed the hybrid recommender system, which is a combination of collaborative filtering and content?based filtering. Content?based filtering recommends the products whose attributes are similar to those of the products that the target customers prefer. However, the hybrid method is used only for the limited categories of products such as music and movie, which are the products whose attributes are easily extracted. Therefore it is essential to find a more effective approach to recommend to customers new products in any category. In this study, we propose a new recommendation method which applies centrality concept widely used to analyze the relational and structural characteristics in social network analysis. The new products are recommended to the customers who are highly likely to buy the products, based on the analysis of the relationships among products by using centrality. The recommendation process consists of following four steps; purchase similarity analysis, product network construction, centrality analysis, and new product recommendation. In order to evaluate the performance of this proposed method, sales data from H department store, one of the well?known department stores in Korea, is used.

Applying Centrality Analysis to Solve the Cold-Start and Sparsity Problems in Collaborative Filtering

Yoonho Cho(조윤호),Jounghae Bang(방정혜) 한국지능정보시스템학회 2011 지능정보연구 Vol.17 No.3

Collaborative Filtering (CF) suffers from two major problems:sparsity and cold-start recommendation. This paper focuses on the cold-start problem for new customers with no purchase records and the sparsity problem for the customers with very few purchase records. For the purpose, we propose a method for the new customer recommendation by using a combined measure based on three well-used centrality measures to identify the customers who are most likely to become neighbors of the new customer. To alleviate the sparsity problem, we also propose a hybrid approach that applies our method to customers with very few purchase records and CF to the other customers with sufficient purchases. To evaluate the effectiveness of our method, we have conducted several experiments using a data set from a department store in Korea. The experiment results show that the combination of two measures makes better recommendations than not only a single measure but also the best-seller-based method and that the performance is improved when applying the hybrid approach.

가솔린엔진 대상 성능시험시의 노킹보정률을 사용한 엔진 수정토크의 편차개선

조윤호(Yoonho Cho),김용옥(Yongok Kim),이춘우(Chunwoo Lee),김우태(Wootai Kim) 한국자동차공학회 2006 한국자동차공학회 춘 추계 학술대회 논문집 Vol.- No.-

Recent trends of development in small size gasoline engines are both to have higher compression ratio for the purpose of improved fuel consumption and to advance spark timing up to DBL in a low to mid engine speed region for a good acceleration performance of vehicles. However, there occurs the deviation of corrected engine torque results during engine performance test on dynamometer because test conditions influence the onset of knock. Therefore, this research shows the test deviation of corrected engine torque decreases when knock correction rate is used.

청평-양평 지역에 분포하는 근청석 편마암의 백립암상 변성작용과 P-T 진화 경로

조윤호(Yoonho Cho),조문섭(Moonsup Cho),이승렬(Seung Ryeol Lee) 한국암석학회 1996 암석학회지 Vol.5 No.1

사회연결망분석과 인공신경망을 이용한 추천시스템 성능 예측

조윤호(Yoonho Cho),김인환(Inhwan Kim) 한국지능정보시스템학회 2010 지능정보연구 Vol.16 No.4

협업필터링 추천은 다양한 분야에서 활용되고 있지만 트랜잭션 데이터의 성격에 따라 추천 성능에 현저한 차이를 보이고 있다. 기존 연구에서는 이러한 추천 성능의 차이가 나타나는 이유에 대한 설명을 구체적으로 제시하지 못하고 있고 이에 따라 추천 성능의 예측 또한 연구된 바가 없다. 본 연구는 사회네트워크분석과 인공신경망 모형을 이용하여 협업필터링 추천시스템의 성능을 예측하고자 한다. 본 연구의 목적을 달성하기 위해 국내 백화점의 트랜잭션 데이터를 기반으로 형성되는 고객간 사회 네트워크의 구조적 지표를 측정한 후 이를 기반으로 인공신경망 모형을 구축하고 검증한다. 본 연구는 협업필터링 추천 성능을 예측할 수 있는 새로운 모형을 제시하였다는 점에서 그 의의가 있으며 이를 통해 기업들의 협업필터링 추천시스템 도입에 대한 의사결정에 도움을 줄 수 있을 것으로 기대된다.

[가솔린엔진부문] EGR 장착 스파크 점화 LPG 엔진의 성능 및 배기특성

조윤호(Yoonho Cho),구준모(Junemo Koo),김정헌(Jeongheon Kim),김승규(Seunggyu Kim),배충식(Choongsik Bae),오승묵(Seungmook Oh),강건용(Kemyong Kang) 한국자동차공학회 2000 한국자동차공학회 춘 추계 학술대회 논문집 Vol.- No.-

EGR (Exhaust Gas Recirculation) system has been used 10 reduce NO, emissions, improve fuel economy, and decrease thermal loading of engine because it offers the benefits of charge dilution as is the case with a lean bum technique. It is currently used in conventional engines, especially light-duty gasoline and diesel engines for a variety of advantages, and in recent years, it has become as a means of reducing engine-out emissions for heavy-duty vehicles as a consequence of the development of its control schemes as well.<br/> However, the occurrence of excessive cyclic variation with high EGR rates, especially at high load conditions, brings about the undesirable combustion instability within the engine cylinder, which results in the deterioration of both engine performance and emissions. Therefore, in order to avoid the reduction of thermal efficiency and to improve fuel economy, the optimum EGR rate depending on operating conditions of engine, should be derived effectively.<br/> An experimental study was conducted to investigate the effects of EGR on performance and emission characteristics of a spark-ignition LPG fuelled engine, and the feasibility of an enhanced methodology, such as a cooled EGR system.<br/>

EGR 장착 스파크 점화 LPG 엔진의 성능 및 배기특성

조윤호(Yoonho Cho),구준모(Junemo Koo),장진영(Jinyoung Jang),배충식(Choongsik Bae) 한국자동차공학회 2002 한국 자동차공학회논문집 Vol.10 No.1

An experimental study was conducted to investigate the effects of EGR (Exhaust Gas Recirculation) variables on performance and emission characteristics in a 2-liter 4-cylinder spark-ignition LPG fuelled engine. The effects of EGR on the reduction of thermal loading at exhaust manifold were also investigated because the reduced gas temperature is desirable for the reliability of an engine in light of both thermal efficiency and material issue of exhaust manifold. The steady-state tests show that the brake thermal efficiency increased and the brake specific fuel consumption decreased with the increase of EGR rate in hot EGR and with the decrease of EGR temperature in case of cooled EGR, while the stable combustion was maintained. The increase of EGR rate or the decrease of EGR temperature results in the reduction of NOx emission even in the increase of HC emission. Furthermore, decreasing EGR temperature by 180℃ enabled the reduction of exhaust gas tem-perature by 15℃ in cooled EGR test at 1600rpm/370kPa BMEP operation, and consequently the reduction of thermal load at exhaust. The optimization strategy of EGR application is to be discussed by the investigation on the effect of geometrical characteristics of EGR-supplying pipe line.

가솔린엔진 대상 성능시험시의 노킹보정률을 사용한 엔진 수정토크의 편차개선

조윤호(Yoonho Cho),김우태(Wootai Kim),배충식(Choongsik Bae) 한국자동차공학회 2008 한국 자동차공학회논문집 Vol.16 No.4

Recent trends of development in small size gasoline engines are both to have higher compression ratio for the purpose of improved fuel consumption and to advance spark timing up to DBL in a low to mid engine speed region for a good acceleration performance of vehicles. However, there occurs the deviation of corrected engine torque results during engine performance test on dynamometer because test conditions influence the onset of knock. Therefore, this research shows the test deviation of corrected engine torque decreases when knock correction rate is used.

GDI 엔진의 PN 생성 원인 규명 및 저감에 대한 연구

유철호(Chulho Yu),조윤호(Yoonho Cho),김진남(Jinnam Kim),황인구(Ingoo Hwang),오대윤(Daeyoon Oh) 한국자동차공학회 2012 한국자동차공학회 부문종합 학술대회 Vol.2012 No.5

Fuel injected directly in cylinder in Gasoline Direct Injection engine is changed partly to particulate. It mast be reduced to meet the regulation of particulate number in EURO 6. Particulate is created in diffusion flame of wall wetting piston and combustion chamber roof, fuel deposits around injector tip, residual fuel droplets during combustion and also in the inhomogeneous gas phase around local rich area and the stored fuel in fire land. PN level can be reduced by the optimization of mapping parameter, the increase of injection pressure, multiple injection and laser drilled injection hole in injector. Also, MPI injectors are installed in ports together with GDI injectors and are used in the area that emits a lot of particulate instead of or together with GDI injectors. The source and emission level of particulate in GDI engine is investigated and the methods for reducing particulate are investigated in the paper.

빅데이터 기반 추천시스템 구현을 위한 다중 프로파일 앙상블 기법

김민정(Minjeong Kim),조윤호(Yoonho Cho) 한국지능정보시스템학회 2015 지능정보연구 Vol.21 No.4

The recommender system is a system which recommends products to the customers who are likely to be interested in. Based on automated information filtering technology, various recommender systems have been developed. Collaborative filtering (CF), one of the most successful recommendation algorithms, has been applied in a number of different domains such as recommending Web pages, books, movies, music and products. But, it has been known that CF has a critical shortcoming. CF finds neighbors whose preferences are like those of the target customer and recommends products those customers have most liked. Thus, CF works properly only when there’s a sufficient number of ratings on common product from customers. When there’s a shortage of customer ratings, CF makes the formation of a neighborhood inaccurate, thereby resulting in poor recommendations. To improve the performance of CF based recommender systems, most of the related studies have been focused on the development of novel algorithms under the assumption of using a single profile, which is created from users rating information for items, purchase transactions, or Web access logs. With the advent of big data, companies got to collect more data and to use a variety of information with big size. So, many companies recognize it very importantly to utilize big data because it makes companies to improve their competitiveness and to create new value. In particular, on the rise is the issue of utilizing personal big data in the recommender system. It is why personal big data facilitate more accurate identification of the preferences or behaviors of users. The proposed recommendation methodology is as follows: First, multimodal user profiles are created from personal big data in order to grasp the preferences and behavior of users from various viewpoints. We derive five user profiles based on the personal information such as rating, site preference, demographic, Internet usage, and topic in text. Next, the similarity between users is calculated based on the profiles and then neighbors of users are found from the results. One of three ensemble approaches is applied to calculate the similarity. Each ensemble approach uses the similarity of combined profile, the average similarity of each profile, and the weighted average similarity of each profile, respectively. Finally, the products that people among the neighborhood prefer most to are recommended to the target users. For the experiments, we used the demographic data and a very large volume of Web log transaction for 5,000 panel users of a company that is specialized to analyzing ranks of Web sites. R and SAS E-miner was used to implement the proposed recommender system and to conduct the topic analysis using the keyword search, respectively. To evaluate the recommendation performance, we used 60% of data for training and 40% of data for test. The 5-fold cross validation was also conducted to enhance the reliability of our experiments. A widely used combination metric called F1 metric that gives equal weight to both recall and precision was employed for our evaluation. As the results of evaluation, the proposed methodology achieved the significant improvement over the single profile based CF algorithm. In particular, the ensemble approach using weighted average similarity shows the highest performance. That is, the rate of improvement in F1 is 16.9 percent for the ensemble approach using weighted average similarity and 8.1 percent for the ensemble approach using average similarity of each profile. From these results, we conclude that the multimodal profile ensemble approach is a viable solution to the problems encountered when there’s a shortage of customer ratings. This study has significance in suggesting what kind of information could we use to create profile in the environment of big data and how could we combine and utilize them effectively. However, our methodology should be further studied to consider for its r