온라인 리뷰의 제목과 내용의 일치성이 리뷰 유용성에 미치는 영향

이청용,김재경,Li, Qinglong,Kim, Jaekyeong 한국지식경영학회 2022 지식경영연구 Vol.23 No.3

Many studies have proposed several factors that affect review helpfulness. Previous studies have investigated the effect of quantitative factors (e.g., star ratings) and affective factors (e.g., sentiment scores) on review helpfulness. Online reviews contain titles and contents, but existing studies focus on the review content. However, there is a limitation to investigating the factors that affect review helpfulness based on the review content without considering the review title. However, previous studies independently investigated the effect of review content and title on review helpfulness. However, it may ignore the potential impact of similarity between review titles and content on review helpfulness. This study used text consistency between review titles and content affect review helpfulness based on the mere exposure effect theory. We also considered the role of information clearness, review length, and source reliability. The results show that text consistency between the review title and the content negatively affects the review helpfulness. Furthermore, we found that information clearness and source reliability weaken the negative effects of text consistency on review helpfulness.

사용자의 선호도 정보를 활용한 직무 추천 시스템 연구

이청용(Qinglong Li),전상홍(Sanghong Jeon),이창재(Changjae Lee),김재경(Jae Kyeong Kim) 한국IT서비스학회 2021 한국IT서비스학회지 Vol.20 No.3

Recently, online job websites have been activated as unemployment problems have emerged as social problems and demand for job openings has increased. However, while the online job platform market is growing, users have difficulty choosing their jobs. When users apply for a job on online job websites, they check various information such as job contents and recruitment conditions to understand the details of the job. When users choose a job, they focus on various details related to the job rather than simply viewing and supporting the job title. However, existing online job websites usually recommend jobs using only quantitative preference information such as ratings. However, if recommendation services are provided using only quantitative information, the recommendation performance is constantly deteriorating. Therefore, job recommendation services should provide personalized services using various information about the job. This study proposes a recommended methodology that improves recommendation performance by elaborating on qualitative preference information, such as details about the job. To this end, this study performs a topic modeling analysis on the job content of the user profile. Also, we apply LDA techniques to explore topics from job content and extract qualitative preferences. Experiments show that the proposed recommendation methodology has better recommendation performance compared to the traditional recommendation methodology.

CNN 기반 리뷰 유용성 점수 예측을 통한 개인화 추천 서비스 성능 향상에 관한 연구

이청용(Qinglong Li),이병현(Byunghyun Lee),이흠철(Xinzhe Li),김재경(Jae Kyeong Kim) 한국지능정보시스템학회 2021 지능정보연구 Vol.27 No.3

Recently, various types of products have been launched with the rapid growth of the e-commerce market. As a result, many users face information overload problems, which is time-consuming in the purchasing decision-making process. Therefore, the importance of a personalized recommendation service that can provide customized products and services to users is emerging. For example, global companies such as Netflix, Amazon, and Google have introduced personalized recommendation services to support users purchasing decisions. Accordingly, the users information search cost can reduce which can positively affect the companys sales increase. The existing personalized recommendation service research applied Collaborative Filtering (CF) technique predicts user preference mainly use quantified information. However, the recommendation performance may have decreased if only use quantitative information. To improve the problems of such existing studies, many studies using reviews to enhance recommendation performance. However, reviews contain factors that hinder purchasing decisions, such as advertising content, false comments, meaningless or irrelevant content. When providing recommendation service uses a review that includes these factors can lead to decrease recommendation performance. Therefore, we proposed a novel recommendation methodology through CNN-based review usefulness score prediction to improve these problems. The results show that the proposed methodology has better prediction performance than the recommendation method considering all existing preference ratings. In addition, the results suggest that can enhance the performance of traditional CF when the information on review usefulness reflects in the personalized recommendation service.

명시적 및 암시적 피드백을 활용한 그래프 컨볼루션 네트워크 기반 추천 시스템 개발

이흠철(Xinzhe Li),김동언(Dongeon Kim),이청용(Qinglong Li),김재경(JaeKyeong Kim) 한국IT서비스학회 2023 한국IT서비스학회지 Vol.22 No.1

With the development of the e-commerce market, various types of products continue to be released. However, customers face an information overload problem in purchasing decision-making. Therefore, personalized recommendations have become an essential service in providing personalized products to customers. Recently, many studies on GCN-based recommender systems have been actively conducted. Such a methodology can address the limitation in disabling to effectively reflect the interaction between customer and product in the embedding process. However, previous studies mainly use implicit feedback data to conduct experiments. Although implicit feedback data improves the data scarcity problem, it cannot represent customers' preferences for specific products. Therefore, this study proposed a novel model combining explicit and implicit feedback to address such a limitation. This study treats the average ratings of customers and products as the features of customers and products and converts them into a high-dimensional feature vector. Then, this study combines ID embedding vectors and feature vectors in the embedding layer to learn the customer-product interaction effectively. To evaluate recommendation performance, this study used the MovieLens dataset to conduct various experiments. Experimental results showed the proposed model outperforms the state-of-the-art. Therefore, the proposed model in this study can provide an enhanced recommendation service for customers to address the information overload problem.

댓글 특성 기반 고객 프로파일 구축을 통한 개인화 추천 서비스에 관한 연구

엄금철(JinZhe Yan),이가은(JiaEn Li),이청용(QingLong Li) 한국디지털콘텐츠학회 2020 한국디지털콘텐츠학회논문지 Vol.21 No.9

Global e-commerce companies offer personalized recommendation services to gain sustainable competitive advantages. Previous personalized recommendation service studies mainly used customer’s explicit rating and implicit data to predict customer preferences. However, the recommendation accuracy is low when only using quantitative data. To overcome the limitations of the existing recommender system that only considers customer ratings, the authors proposed a novel method to recommend products to customers using review data that represent their qualitative preferences. This study proposed a CRSE model and created a new customer profile. The experiment results show that the predictive accuracy of the proposed methods is superior to existing recommended techniques.

사용자의 정성적 선호도와 정량적 선호도를 고려하는 추천 시스템 성능 향상에 관한 연구

이승우(Seungwoo Lee),강경모(Kyungmo Kang),이병현(Byunghyun Lee),이청용(Qinglong Li),김재경(Jaekyeong Kim) 한국경영과학회 2022 經營 科學 Vol.39 No.1

With the recent rapid development of ICT (Information and Communication Technology) and mobile devices, most users receive various types of information. Thus, users would face information overload issues, which takes much time to select products and services they need or prefer. Therefore, a personalized recommender system has become a practical methodology to address such issues. Existing studies mainly utilized quantitative preferences (e.g., star ratings, click). However, such methodology has limitations in that quantitative information can not fully reflect the user"s preference. Therefore, we proposed a novel recommender system methodology that utilized quantitative and qualitative preferences information. To evaluate the performance of the proposed methodology we collected the real-world dataset that contains 771,824 reviews, 648,210 users, and 470 hotels on Tripadvisor.com. The performance of the proposed methodology using quantitative and qualitative preferences information showed better performance than quantitative preferences.