개인화 서비스 진전에 따른 자동추천 시스템 연구 동향과 방법론적 특성 연구

황용석(Yong-suk, Hwang),김기태(Ki-Tae, Kim) 사이버커뮤니케이션학회 2019 사이버 커뮤니케이션 학보 Vol.36 No.2

본 연구의 목적은 사회과학 연구자들을 위한 추천 시스템 연구의 연구 경향의 변화와 방법론적 동향을 탐색하는 것이다. 각각의 연구 경향에서 사용된 연구방법상의 문제점과 한계를 검토하고 추천 시스템 연구 범위의 확장과 더불어 새롭게 사용되는 연구방법들을 제시한다. 전통적인 추천 시스템 연구 경향은 크게 시스템 중심 접근법과 이용자 중심 접근법으로 나눌 수 있다. 그런데 시스템 중심 접근법의 주요 관심사여왔던 추천서비스 알고리즘의 예측 정확도 향상이 반드시 실제 이용자의 사용 만족도로 이어지지 않는다는 비판이 지속적으로 제기되었다. 이러한 이유에서 등장한 이용자 중심 접근법은 주로 전통적인 사회과학 실험 방법(lab experiments)을 사용하는데, 이러한 연구방법 역시 제한된 수의 피실험자들에게 제한된 변수만을 테스트하는 방식으로 도출된 결과의 낮은 신뢰도와 닞은 외적 타당성이 단점으로 지적되어왔다. 이 글에서는 현재 주요 글로벌 미디어 기업들(구글, 아마존, 페이스북 등)이 적극적으로 활용하고 있는 대규모 온라인 통제실험을 이러한 이용자 중심 접근법의 실험 방법론이 가지고 있는 약점을 극복할 수 있는 대안적 방법론으로 제시한다. 한편 추천서비스가 점점 우리 일상의 한 부분이 되어가면서, 기존의 추천 시스템 평가 위주의 연구영역에서 벗어나 추천 시스템의 사회적 영향에 대한 관심으로 연구 영역이 확장되었다. 이러한 연구 영역의 확장과 더불어 새로운 방법론적 시도가 이루어졌는데, 이 글에서는 온라인 뉴스 추천서비스 사용자들의 파편화 문제를 관계망 분석을 통해 다룬 일련의 연구들과 추천 알고리즘의 편향과 차별 문제에 데이터마이닝 기법과 대규모 온라인 통제실험방법을 접목한 연구들과 연구에 사용된 몇 가지 기법을 소개한다. The main purpose of this paper is to explore the recent trends of recommendation system research and methodological changes. This paper examined the issues and limitations of the research methods that each study in the filed employed. At the same time, this study suggested the newly emerging methodological trends, as the research area of recommendation system studies expands. Traditional recommendation studies are categorized into two approaches: system-centric approaches and user-centric approaches. However, recommendation system researchers suggested that the improvement of the prediction accuracy does not necessarily lead to user satisfaction of the system, which resulted in the changes in the approaches to the recommendation system studies from system-centric approaches to user-centric approaches. User centric approaches tend to involve traditional user experiments, with the low level of consistency across studies and external validity of the research design. Consequently, this paper suggested, as an alternative method, online controlled experiments at large scale which have been widely used by the major global media cooperation, such as Google, Amazon, Facebook. As recommendation systems has been integrated into our daily life, the area of interest in the field expanded to the issue of social influence of recommendation systems beyond the issue of evaluation of recommendation system, such as the issues of the fragmentation of the user groups of online news recommendation services and the bias and discrimination of the recommendation algorithm Together with such expansion of the research scope, new methodological attempt has been made. This paper examined how the newly emerging issue in recommendation research field are aligned with new research or analytic methods such as network analysis and data mining techniques.

개인화 추천시스템에서 고객 제품 리뷰가 사회적 실재감에 미치는 영향

최재원(Jaewon Choi),이홍주(Hong Joo Lee) 한국지능정보시스템학회 2011 지능정보연구 Vol.17 No.3

Many online stores bring features that can build trust in their customers. More so, the number of products or content services on online stores has been increasing rapidly. Hence, personalization on online stores is considered to be an important technology to companies and customers. Recommender systems that provide favorable products and customer product reviews to users are the most commonly used features in this purpose. There are many studies to that investigated the relationship between social presence as an antecedent of trust and provision of recommender systems or customer product reviews. Many online stores have made efforts to increase perceived social presence of their customers through customer reviews, recommender systems, and analyzing associations among products. Primarily because social presence can increase customer trust or reuse intention for online stores. However, there were few studies that investigated the interactions between recommendation type, product type and provision of customer product reviews on social presence. Therefore, one of the purposes of this study is to identify the effects of personalized recommender systems and compare the role of customer reviews with product types. This study performed an experiment to see these interactions. Experimental web pages were developed with 2×2 factorial setting based on how to provide social presence to users with customer reviews and two product types such as hedonic and utilitarian. The hedonic type was a ringtone chosen from Nate.com while the utilitarian was a TOEIC study aid book selected from Yes24.com. To conduct the experiment, web based experiments were conducted for the participants who have been shopping on the online stores. Participants were a total of 240 and 30% of the participants had the chance of getting the presents. We found out that social presence increased for hedonic products when personalized recommendations were given compared to non?personalized recommendations. Although providing customer reviews for two product types did not significantly increase social presence, provision of customer product reviews for hedonic (ringtone) increased perceived social presence. Otherwise, provision of customer product reviews could not increase social presence when the systems recommend utilitarian products (TOEIC study aid books). Therefore, it appears that the effects of increasing perceived social presence with customer reviews have a difference for product types. In short, the role of customer reviews could be different based on which product types were considered by customers when they are making a decision related to purchasing on the online stores. Additionally, there were no differences for increasing perceived social presence when providing customer reviews. Our participants might have focused on how recommendations had been provided and what products were recommended because our developed systems were providing recommendations after participants rating their preferences. Thus, the effects of customer reviews could appear more clearly if our participants had actual purchase opportunity for the recommendations. Personalized recommender systems can increase social presence of customers more than nonpersonalized recommender systems by using user preference. Online stores could find out how they can increase perceived social presence and satisfaction of their customers when customers want to find the proper products with recommender systems and customer reviews. In addition, the role of customer reviews of the personalized recommendations can be different based on types of the recommended products. Even if this study conducted two product types such as hedonic and utilitarian, the results revealed that customer reviews for hedonic increased social presence of customers more than customer reviews for utilitarian. Thus, online stores need to consider the role of providing customer reviews with highly personalized in

공동물류 환경의 혼합추천시스템 기반 차주-화주 중개서비스 구현

장상영(Sangyoung Jang),최명진(Myoungjin Choi),양재경(Jaekyung Yang) 한국산업경영시스템학회 2016 한국산업경영시스템학회지 Vol.39 No.4

Compound logistics is a service aimed to enhance logistics efficiency by supporting that shippers and consigners jointly use logistics facilities. Many of these services have taken place both domestically and internationally, but the joint logistics services for e-commerce have not been spread yet, since the number of the parcels that the consigners transact business is usually small. As one of meaningful ways to improve utilization of compound logistics, we propose a brokerage service for shipper and consigners based on the hybrid recommendation system using very well-known classification and clustering methods. The existing recommendation system has drawn a relatively low satisfaction as it brought about one-to-one matches between consignors and logistics vendors in that such matching constrains choice range of the users to one-to-one matching each other. However, the implemented hybrid recommendation system based brokerage agent service system can provide multiple choice options to mutual users with descending ranks, which is a result of the recommendation considering transaction preferences of the users. In addition, we applied feature selection methods in order to avoid inducing a meaningless large size recommendation model and reduce a simple model. Finally, we implemented the hybrid recommendation system based brokerage agent service system that shippers and consigners can join, which is the system having capability previously described functions such as feature selection and recommendation. As a result, it turns out that the proposed hybrid recommendation based brokerage service system showed the enhanced efficiency with respect to logistics management, compared to the existing one by reporting two round simulation results.

THE ASYMMETRIC EFFECTS OF ATTITUDE TOWARD THE BRAND (SYMBOLIC vs. FUNCTIONAL) UPON RECOMMENDATION SYSTEM (ARTIFICIAL INTELLIGENCE vs. HUMAN)

Kiwan Park,Yaeri Kim,Seojin Stacey Lee 글로벌지식마케팅경영학회 2018 Global Marketing Conference Vol.2018 No.07

New product entails risk, causing resistance to adoption. The recommendation system may decrease the psychological risk by guiding decision making process to be more efficient (H?ubl and Trifts, 2000). AI (Artificial Intelligence) has been getting smarter and smarter and widely applied to the recommendation system. Even while you are browsing on your Facebook, AI recommends you the products that you may like based on the customized analysis of your interest. However, do people always love to adopt the smart recommends from AI? Definitely no! Then when and why people reluctantly accept AI recommendation? We assume that the product or service where the sense and feeling is important, people might be reluctant to accept the recommendation from artificial intelligence. This is because people might feel threatened when the AI challenges against human uniqueness (Gray and Wegner, 2012). Thus, in this study we investigated how the recommendation system types (AI vs. Human) affect brand attitude depending on the brand image (Symbolic vs. Functional). We found consumers are reluctant to accept a recommendation from AI in symbolic brand where human sense and feel are considered to be critical factors (Study1). This effect was further explained by uncanny-feeling toward the AI recommendation system (Study2). This research is meaningful in that it is the first attempt to apply the artificial intelligence recommendation concept to the marketing strategy by incorporating the concept of brand image. We predicted and found AI based recommendation system is reluctantly accepted for symbolic brand. Furthermore, we discovered the underlying process for this phenomenon as uncanny feeling. People seemed to have uncomfortable feelings against AI recommendation when the brand image is related to sense and feel considered as nature of human uniqueness. Thus, marketers should be very cautious when utilizing the AI recommendation system not to threaten human uniqueness area.

RFM 다차원 분석 기법을 활용한 암시적 사용자 피드백 기반 협업 필터링 개선 연구

이재성,김재영,강병욱 한국지능정보시스템학회 2019 지능정보연구 Vol.25 No.1

The utilization of the e-commerce market has become a common life style in today. It has become important part to know where and how to make reasonable purchases of good quality products for customers. This change in purchase psychology tends to make it difficult for customers to make purchasing decisions in vast amounts of information. In this case, the recommendation system has the effect of reducing the cost of information retrieval and improving the satisfaction by analyzing the purchasing behavior of the customer. Amazon and Netflix are considered to be the well-known examples of sales marketing using the recommendation system. In the case of Amazon, 60% of the recommendation is made by purchasing goods, and 35% of the sales increase was achieved. Netflix, on the other hand, found that 75% of movie recommendations were made using services. This personalization technique is considered to be one of the key strategies for one-to-one marketing that can be useful in online markets where salespeople do not exist. Recommendation techniques that are mainly used in recommendation systems today include collaborative filtering and content-based filtering. Furthermore, hybrid techniques and association rules that use these techniques in combination are also being used in various fields. Of these, collaborative filtering recommendation techniques are the most popular today. Collaborative filtering is a method of recommending products preferred by neighbors who have similar preferences or purchasing behavior, based on the assumption that users who have exhibited similar tendencies in purchasing or evaluating products in the past will have a similar tendency to other products. However, most of the existed systems are recommended only within the same category of products such as books and movies. This is because the recommendation system estimates the purchase satisfaction about new item which have never been bought yet using customer's purchase rating points of a similar commodity based on the transaction data. In addition, there is a problem about the reliability of purchase ratings used in the recommendation system. Reliability of customer purchase ratings is causing serious problems. In particular, 'Compensatory Review' refers to the intentional manipulation of a customer purchase rating by a company intervention. In fact, Amazon has been hard-pressed for these "compassionate reviews" since 2016 and has worked hard to reduce false information and increase credibility. The survey showed that the average rating for products with 'Compensated Review' was higher than those without 'Compensation Review'. And it turns out that 'Compensatory Review' is about 12 times less likely to give the lowest rating, and about 4 times less likely to leave a critical opinion. As such, customer purchase ratings are full of various noises. This problem is directly related to the performance of recommendation systems aimed at maximizing profits by attracting highly satisfied customers in most e-commerce transactions. In this study, we propose the possibility of using new indicators that can objectively substitute existing customer 's purchase ratings by using RFM multi-dimensional analysis technique to solve a series of problems. RFM multi-dimensional analysis technique is the most widely used analytical method in customer relationship management marketing(CRM), and is a data analysis method for selecting customers who are likely to purchase goods. As a result of verifying the actual purchase history data using the relevant index, the accuracy was as high as about 55%. This is a result of recommending a total of 4,386 different types of products that have never been bought before, thus the verification result means relatively high accuracy and utilization value. And this study suggests the possibility of general recommendation system that can be applied to various offline product data. If additional data is acquired in the future, the accuracy of... 전자상거래 시장의 이용이 보편화 되며 고객들에게 좋은 품질의 물건을 어디서, 얼마나 합리적으로 구매할수 있는지가 중요해졌다. 이러한 구매 심리의 변화는 방대한 정보 속에서 오히려 고객들의 구매 의사결정을 어렵게 만드는 경향이 있다. 이때 추천 시스템은 고객의 구매 행동을 분석하여 정보 검색에 드는 비용을 줄이고만족도를 높이는 효과가 있다. 하지만 대부분 추천 시스템은 책이나 영화 등 동종 상품 분류 내에서만 추천이이뤄진다. 왜냐하면 추천 시스템은 특정 상품에 매긴 구매 평점 데이터를 기반으로 해당 상품 분류 내 유사한상품에 대한 구매 만족도를 추정하기 때문이다. 그밖에 추천 시스템에서 사용하는 구매 평점의 신뢰성에 대한문제도 제시되고 있으며 오프라인에선 평점 확보 자체가 어렵다. 이에 본 연구에서는 일련의 문제를 개선하기위해 RFM 다차원 분석 기법을 활용하여 기존에 사용하던 고객의 구매 평점을 객관적으로 대체할 수 있는 새로운 지표의 활용 가능성을 제안하는 바이다. 실제 기업의 구매 이력 데이터에 해당 지표를 적용해서 검증해본결과 높게는 약 55%에 해당하는 정확도를 기록했다. 이는 총 4,386종에 달하는 이종 상품들 중 한번도 이용해본 적 없는 상품을 추천한 결과이기 때문에 검증 결과는 상대적으로 높은 정확도와 활용가치를 의미한다. 그리고 본 연구는 오프라인의 다양한 상품데이터에서도 적용할 수 있는 범용적인 추천 시스템의 가능성을 시사한다. 향후 추가적인 데이터를 확보한다면 제안하는 추천 시스템의 정확도 향상도 기대할 수 있다.

Sentiment Analysis of Online Customer Reviews for Product Recommendation : Comparison with Traditional CF-based Recommendation

Heejin Yang,Yongmoo Suh 한국경영정보학회 2015 한국경영정보학회 학술대회논문집 Vol.2015 No.11

Online product reviews have been an important source for customers to make informed decisions when purchasing goods. Yet, it is nearly impossible for consumers to access all the available reviews online. Such problem could be overcome by employing a recommendation system. Collaborative filtering (CF) recommendation system recommends products based on users’ ratings which may not represent customers’ true opinions on the items they bought. In this study, ratings were substituted with those computed using the frequencies of positive and negative words and expressions obtained from product reviews when developing a sentiment-based recommendation system. The objective of this study is to compare three recommendation systems: traditional CF-based recommendation, sentiment-based recommendation utilizing publicly available lexicon, sentiment-based recommendation employing domain-specific words and expressions examined in the study. The experiments conducted using the data obtained from MakeupAlley.com indicated that sentiment-based recommendation system applying domain-specific words and expressions outperformed the other two systems.

소비자 감성 분석 기반의 음악 추천 알고리즘 개발

이승준,서봉군,박도형 한국지능정보시스템학회 2018 지능정보연구 Vol.24 No.4

Music is one of the most creative act that can express human sentiment with sound. Also, since music invoke people’s sentiment to get empathized with it easily, it can either encourage or discourage people’s sentiment with music what they are listening. Thus, sentiment is the primary factor when it comes to searching or recommending music to people. Regard to the music recommendation system, there are still lack of recommendation systems that are based on customer sentiment. An algorithm’s that were used in previous music recommendation systems are mostly user based, for example, user’s play history and playlists etc. Based on play history or playlists between multiple users, distance between music were calculated refer to basic information such as genre, singer, beat etc. It can filter out similar music to the users as a recommendation system. However those methodology have limitations like filter bubble. For example, if user listen to rock music only, it would be hard to get hip-hop or R&B music which have similar sentiment as a recommendation. In this study, we have focused on sentiment of music itself, and finally developed methodology of defining new index for music recommendation system. Concretely, we are proposing “SWEMS” index and using this index, we also extracted “Sentiment Pattern” for each music which was used for this research. Using this “SWEMS” index and “Sentiment Pattern”, we expect that it can be used for a variety of purposes not only the music recommendation system but also as an algorithm which used for buildup predicting model etc. In this study, we had to develop the music recommendation system based on emotional adjectives which people generally feel when they listening to music. For that reason, it was necessary to collect a large amount of emotional adjectives as we can. Emotional adjectives were collected via previous study which is related to them. Also more emotional adjectives has collected via social metrics and qualitative interview. Finally, we could collect 134 individual adjectives. Through several steps, the collected adjectives were selected as the final 60 adjectives. Based on the final adjectives, music survey has taken as each item to evaluated the sentiment of a song. Surveys were taken by expert panels who like to listen to music. During the survey, all survey questions were based on emotional adjectives, no other information were collected. The music which evaluated from the previous step is divided into popular and unpopular songs, and the most relevant variables were derived from the popularity of music. The derived variables were reclassified through factor analysis and assigned a weight to the adjectives which belongs to the factor. We define the extracted factors as “SWEMS” index, which describes sentiment score of music in numeric value. In this study, we attempted to apply Case Based Reasoning method to implement an algorithm. Compare to other methodology, we used Case Based Reasoning because it shows similar problem solving method as what human do. Using “SWEMS” index of each music, an algorithm will be implemented based on the Euclidean distance to recommend a song similar to the emotion value which given by the factor for each music. Also, using “SWEMS” index, we can also draw “Sentiment Pattern” for each song. In this study, we found that the song which gives a similar emotion shows similar “Sentiment Pattern” each other. Through “Sentiment Pattern”, we could also suggest a new group of music, which is different from the previous format of genre. This research would help people to quantify qualitative data. Also the algorithms can be used to quantify the content itself, which would help users to search the similar content more quickly. 음악은 인간의 감성을 소리로 표현하는 창조적 예술 행위이다. 음악은 사람들의 기분을 우울하게 혹은 기쁘게 변화시킬 수 있다. 따라서 음악을 감상하는 데 있어 감성은 소비자에게 적합한 음악을 찾고 들려주는 데 매우 중요한 요소인데, 다양한 음원 서비스에서 제공하는 추천 알고리즘은 사용자의 기본적인 정보(성별, 나이, 감상 횟수 등)와 사용자의 플레이 기록에 기반한 음악 추천 방식을 주로 사용하고 있다. 본 연구에서는 음악을감상하는 개인의 감성을 고려하여 각 음원이 가지는 고유의 감성을 기본으로 한 음악 추천 알고리즘을 제안해보고자 한다. 구체적으로, 사용자들이 자주 듣는 음악과 그렇지 않은 음악을 기준으로 ‘감정 패턴’을 추출 후 상관관계를 확인하고자 하며, 앞선 결과를 기반으로 사용자들이 원하는 노래에 대한 검색과 사용자 감성 기반 추천 방법을 도출해내보고자 한다. 이를 위해 본 연구에서는 사례기반추론 기법을 이용하여 사람들이 주로 듣는음악과 비슷한 ‘감성 패턴’을 갖는 특정한 곡을 추천해주는 알고리즘을 개발하였다. 먼저, 분석에 필요한 감정형용사를 정리하여 변수화 시키고, 의미 있는 것끼리 묶어 음악 감성지수를 개발하였고, 분석의 대상이 될 음원에 대해 고유의 감성지수 점수를 측정하였다. 마지막으로 도출된 점수의 결과를 통해 유사한 감정 패턴이 나오는 곡들을 유사 곡 리스트로 분류하고 사용자들에게 추천하는 과정을 거친다. 앞선 일련의 과정을 거처 도출된결과는 음원 추천 시스템뿐만 아니라, 인기 있는 곡과 아닌 곡에 영향을 미치는 변수 도출 및 음원 출시 전, 해당 곡의 스트리밍 수 예측 모형 구축 등 다양한 용도로 사용될 수 있을 것으로 기대한다.

Evaluating the Quality of Recommendation System by Using Serendipity Measure

Tserendulam Dorjmaa,신택수 한국지능정보시스템학회 2019 지능정보연구 Vol.25 No.4

Recently, various approaches to recommendation systems have been studied in terms of the quality of recommendation system. A recommender system basically aims to provide personalized recommendations to users for specific items. Most of these systems always recommend the most relevant items of users or items. Traditionally, the evaluation of recommender system quality has focused on the various predictive accuracy metrics of these. However, recommender system must be not only accurate but also useful to users. User satisfaction with recommender systems as an evaluation criterion of recommender system is related not only to how accurately the system recommends but also to how much it supports the user’s decision making. In particular, highly serendipitous recommendation would help a user to find a surprising and interesting item. Serendipity in this study is defined as a measure of the extent to which the recommended items are both attractive and surprising to the users. Therefore, this paper proposes an application of serendipity measure to recommender systems to evaluate the performance of recommender systems in terms of recommendation system quality. In this study we define relevant or attractive unexpectedness as serendipity measure for assessing recommendation systems. That is, serendipity measure is evaluated as the measure indicating how the recommender system can find unexpected and useful items for users. Our experimental results show that highly serendipitous recommendation such as item-based collaborative filtering method has better performance than the other recommendations, i.e. user-based collaborative filtering method in terms of recommendation system quality.

협력필터링과 사회연결망을 이용한 신규고객 추천방법에 대한 연구

신창훈(Chang-Hoon Shin),이지원(Ji-Won Lee),양한나(Han-Na Yang),최일영(Il Young Choi) 한국지능정보시스템학회 2012 지능정보연구 Vol.18 No.4

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. Thu

조선 전기 유일 천거제의 운영과 그 의의

최선혜 조선시대사학회 2011 朝鮮時代史學報 Vol.56 No.-

The goal of this study is to research the recommendation system(薦擧制度) as the function of the course into official circle for the early Chosŏn and especially to understand the status system of the Chosŏn Dynasty, through the analysis of the nominee's status. Under such a purpose, I have studied an enforcement of the recommendation system of the 'Hermits'(遺逸) in the early Chosŏn and its significance from this writing. For this reason, I took note of the nominee's status. Ranging from the Dynasty's founding to the age of King Sŏngjong in 15th century, I have studied the ruling class of the Chosŏn by analyzing the nominee's status. In general, 'Hermits' is a person who has sufficient qualifications and ability as a administration official with good character and scholarship, but they are not come out to official post, only staying in the wilderness. In some case, there were a person who did not have any intention to be a official among them. But usually they were reluctant to provide their own doing even if there was an intention to become an official. Therefore, the king and incumbent officials were obligated to search for a 'Hermits' by asking all around and to appoint them. The way which selects and appoints them was the recommendation system. The king of the Chosŏn Dynasty ordered to recommend the hermit in frequency. The necessary qualification for recommendee was a personality, intellectual capacity, executive ability and martial arts as well as a non-incumbent official. Actually, in the early Chosŏn Dynasty if we consider the officials career of the 'Hermits' who is recommended, they had got no particular career to speak of. However, the age of King Sŏngjong in the late 15th century marked the beginning of subtle change in the recommendation system. It happened that a man who have been appointed to the official post in the past have been reinstated or promoted by recommendation. Like these tendency came to be stronger as time goes by. After the age of King Sŏngjong, to express the nominee with the commoner had gradually disappeared because of nominee's list was gradually filled with former official. The most of title which calls them was mostly 'literati-scholars'. Further more, they did not make the recommend for the 'Hermits' but rather began to recommend the 'former officials' or a 'Saengwon․Jinsa'(a person who has only passed the first examination for official : 生員․進士), could not trace the 'Hermits' in their own capacities by way of excuse. To be an official by recommendation, that is to say a person who can appoint as a official was limited only 'literati-scholars(士)' not all of 'Yangin'(良人). It was becoming increasingly clear that in the recommendation system, there was any prerequisite qualifications necessary to be a nominee. Since the appearance of the 'Sajok'(士族), 'Yangin' classes was divided into the general public(良人) and the 'Sajok' and the person who will be nominated with the official post came to be provided with 'scholar'. And 'literati-scholars' possessed both personality and learning and they were a man of authority and legitimacy as a ruler. Therefore they behaved as a director of the edification in the region. The more 'Sajok' of the Chosŏn has aimed the moralism policy, recommendation of the 'Hermits' have been forced into a greater emphasis on morality because it is emphasizing that the ruler is expected to have a higher sense of ethics than others. Stratum identification began to form down based on morality as a ruler in the 'Sajok'. Such an tendency have gradually been changed the 'Sajok' into a kind of social status. 이 글의 목적은 초야에 숨어있는 현자를 등용한다는 遺逸薦擧의 운영과 그 변화를 통해 조선시대 지배계층의 성격을 이해해 보는데 있다. 천거는 그 개인의 도덕, 학식, 덕망 등이 관료가 되기에 합당하다는 보다 많은 사람의 평가를 요하는 일이었다. 이 점에서 천거제도를 운영한 사람이 과연 어떤 사람들을 치자로서의 자격이 있다고 판단하여 천거하였는지, 피천자는 어떠한 사람이었는지 등을 분석하는 일은 조선시대 지배계층의 성격을 한층 드러내는 작업이라는 기대이다. 천거는 적절한 시험과 국왕의 재가 등이 있어야 하지만 기본적으로 이미 관직을 가진 사람이 관직에 오를 사람을 추천하는 방식이었다. 조선 초기에 피천자의 자격으로 제시된 조건은 인품, 학식, 행정적 능력, 군사적 능력 등이었다. 유일을 천거하라는 교령이 자주 내려졌지만, 그 조건에 관직을 가진 경력이 있어야 한다든지, 선비(士)이어야 한다든지 등을 못 박지는 않았다. 이 점에서 治者로서의 자격을 갖추었다고 판단된 피천자들을 법제적으로 구분된 별도의 신분에서 선택하는 상황은 아니었다. 15세기 후반 성종 대에 이르면 유일천거에 변화가 일기 시작하였다. 유일천거가 숨어있어 세상에 드러나지 않은 ‘어진 이’를 기용하자는 데에서 한 걸음 좁혀 일정한 문지와 경력을 갖추고 탁용되기를 기다리는 ‘선비’를 지목하는 경향이 두드러졌다. 성종 대에 이르러 사림의 등장과 더불어 국왕과 관료로부터 백성에 이르기까지 모든 사람들이 유교적 이념을 실천하는 국가를 이루자는 이상은 더욱 강조되었다. 현실적으로 어려운 일일지라도 국왕과 관료, 그리고 사족들은 유교이념을 앞서 실천하며 모든 사람을 몰아 善으로 나가게 해야 하였다. 그것이 치자의 소임이며 명분이었다. 유교적 이념에 충실한 사람이 중앙에만 있으리란 법은 없었다. 이 때문에 초야에 있는 유교이념의 실천자를 치자로 발탁하는 일은 매우 중요하였다. 그런데 그러한 사람으로 지목되어 온 ‘유일’ 은 이제 ‘선비’(士)로 지칭되었다. 유일을 탁용해야 하는 까닭도 산림의 선비가 벼슬길로 나아오는 풍토를 진작시키기 위해서였다. 사회의 전체 구조 안에서 치자와 잠재적 치자군이 이른바 ‘선비’로 구분되는 계층의식이 강화되었음 볼 수 있다. 즉 성종 대에 이르러 인품, 학식, 도덕 등을 기준으로 治者와 被治者가 구분되는 의식이 두드러졌고, ‘사’에게는 교화의 책임자로 자임하는 계층의식이 확산되어갔다. 도덕성이 치자의 자격임을 보다 강하게 강조하는 사림의 등장과 더불어 유일천거가 더욱 활성화되는 것은 당연한 결과였다. 중앙에 진출한 관료나 향촌의 선비들이 도덕정치를 지향하면서 치자로서 자신들을 구분짓고 사족으로서의 정체성을 강하게 향유하면 할수록 유일천거는 더욱 확산되어 갈 수 밖에 없었다.