The increasing number of technology transfers from public research institutes in Korea has led to a growing demand for patent recommendation platforms for SMEs. This is because selecting the right technology for commercialization is a critical factor in business success. This study developed a patent recommendation system that uses technology transfer data from the past 10 years to recommend patents that are suitable for SMEs. The system was developed in three stages. First, an item-based collaborative filtering system was developed to recommend patents based on the similarities between the patents that SMEs have previously transferred. Next, a content-based recommendation system based on TF-IDF was developed to analyze patent names and recommend patents with high similarity. Finally, a hybrid system was developed that combines the strengths of both recommendation systems. The experimental results showed that the hybrid system was able to recommend patents that were both similar and relevant to the SMEs' interests. This suggests that the system can be a valuable tool for SMEs that are looking to acquire new technologies.

본 논문은 지각된 가치가 적용된 관광 행동의도 정보를 이용한 지능형 클라우드 환경에서의 관광추천시스템을 제안한다. 이 제안 시스템은 관광정보와 관광객의 지각적 가치가 행동의도에 반영되는 실증적 분석 정보를 와이드 앤 딥러닝 기술을 이용하여 관광추천시스템에 적용하였다. 본 제안 시스템은 다양하게 수집할 수 있는 관광 정보와 관광객이 평소에 지각하고 있던 가치와 사람의 행동에서 나타나는 의도를 수집 분석하여 관광 추천시스템에 적용하였다. 이는 기존에 활용되던 다양한 분야의 관광플랫 폼에 관광 정보, 지각된 가치 및 행동의도에 대한 연관성을 분석하고 매핑하여, 실증적 정보를 제공한다. 그리고 관광정보와 관광객의 지각적 가치가 행동의도에 반영되는 실증적 분석 정보를 선형 모형 구성요소와 신경만 구성요소를 합께 학습하여 한 모형에서 암기 및 일반화 모두를 달성할 수 있는 와이드 앤 딥러닝 기술을 이용한 관광추천 시스템을 제시하였고, 파이프라인 동작 방법을 제시하였다. 본 논문에서 제시한 추천시스템은 와이드 앤 딥러닝 모형을 적용한 결과 관광관련 앱 스토어 방문 페이지 상의 앱 가입률이 대조군 대비 3.9% 향상했고, 다른 1% 그룹에 변수는 동일하고 신경망 구조의 깊은 쪽만 사용한 모형을 적용하여 결과 와이드 앤 딥러닝 모형은 깊은 쪽만 사용한 모형 대비해서 가입률을 1% 증가하였다. 또한, 데이터셋에 대해 수신자 조작 특성 곡선 아래 면적(AUC)을 측정하여, 오프라인 AUC 또한 와이드 앤 딥러닝 모형이 다소 높지만 온라인 트래픽에서 영향력이 더 강하다는 것을 도출하였다.

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 (Häubl 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.

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 market for Game is gradually expanding every year, but most of games are disappearing. For this Reason, User have chosen games based on external factors, and users have been not able to choose customized games for users. Thus, This study aim to provide customized game contents to users among 6 content (logic, space, word, nature, body, music) by making a immersion evaluation model using the Csikszentmihalyi’s theory of flow and Gardner's theory of multiple intelligence. First, it is assumed that the definition of immersion can be obtained as an arousal which is an axis of the emotion criterion. Then, after learning the arousal classification model based on the head micro-vibration as emotion signal, the immersion evaluation system is verified through the evaluation with the multi-intelligence theory. As a result, the user can be informed of what contents are most immersed in the content, and allow to choose the game suitable for himself.

오늘날 인터넷의 출현과 확산으로 인하여 정보의 홍수를 이루게 되었고, 고객들은 자신이 원하는 제품이나 서비스를 선택하기 위해서 정보를 탐색하는 작업이 더욱 어려워지게 되었다. 이러한 고객들에게 좀 더 편리하게 자신이 원하는 제품이나 서비스를 선택하도록 도와주는 것이 추천 시스템으로써, 고객 관계 관리의 중요한 부분으로 자리잡게 되었다. 본 연구에서는, 인터넷상의 여행사 사이트 등에서 고객이 여행지를 선택할 때 고객이 관심을 가질만한 여행지를 추천하여 줌

To be successful in increasingly competitive Internet marketplace, it is essential to capture customer loyalty. In this paper, we provide an intelligent agent approach to incorporate human sensibility into an one-to-one recommendation service in cyber shopping mall. Our system exploits human sensibility ergonomics and on-line preference matching technologies to tailor to the customer the suggestion of goods and the description of store catalog. By presenting goods that are consistent with user interests as well as user sensibility, the accuracy and satisfaction of the recommendation service may be improved.

After the Sea Prince oil spill accident in 1995, the korean government has taken a measure to establish an emergency response system and equip clean-up capacity against large spill, major contents of which are as follows: First, Korea Marine Pollution Response Corporation has been established as a non-government organization for recovery of spilled oil in order to improve private response capabilities. Second, clean-up equipments, such as large clean-up vessels and oil fences for the open sea operation has been expanded. Third, a national contingency plan on the large spill accidents has been established compliance with the provisions of Article 6 of the OPRC 1990. However, there exist some problems in the national response system, such as clearly roles definition between government and private agencies; propel amendment of the Marine Pollution Prevention Act to incorporate major contents of the OPRC 1990; and training and exercises of clean-up personnel. With the above problems in mind, this paper reviews the current issues on the national oil pollution response system and recommends policy-making to tackle to those problems.

Purpose: The purpose of this study is to understand the effect of the unique product recommendation system on customer satisfaction. Research design, data and methodology: The survey method used the self-recording way in which the respondents selected for the study and distributed 300 questionnaires, and with due personal care, researchers collected all the distributed questionnaires. Results: The result implies that the characteristics of the product recommendation system should be more secure and developed. Conclusions: The aspects of the product recommendation system were selected as factors of price fairness, accuracy, and quality through previous studies, and the empirical analysis of the effect of the characteristics of the product recommendation system on customer satisfaction was summarized as follows. Among the attributes of the product recommendation system, the attributes of price fairness, accuracy, and quality affect customer satisfaction. Among them, the beta value of quality was the highest, and the effect of quality was the largest among the three factors. Based on the results of the study, the implications for the characteristics of the product recommendation system are summarized as follows. The aspects of the product recommendation system have a positive effect on customer satisfaction, so it is necessary to fill the needs of consumers based on the survey focused on quality