Enhancing e-commerce recommendations through review summarization and multi-embedding feature fusion
Lim, H., Park, S., Li, Q., Li, X., & Kim, J.
Expert Systems, 43(9), e70390
Qinglong Li
Publications
Selected journal articles and conference publications.
Reducing contextual noise in review-based recommendation via aspect term extraction and attention modeling
Lim, H., Li, X., Park, S., Li, Q., & Kim, J.
Information Sciences, 735, 123078
From Sparse Ratings to Rich Representations: Multi-Source User Modeling for Cross-Domain Recommender Systems
Ryu, D., Lee, S.-K., & Li, Q.
Journal of Intelligence and Information Systems, 32(1), 21-47
Instruction‐Tuned Large Language Models for Review Helpfulness Prediction: An Efficient Fine‐Tuning Framework for E‐Commerce Review Understanding
Li, X., Li, Q., & Kim, J.
Expert Systems, 43(3), e70208
What makes a review helpful? A multimodal prediction model in e-commerce
Lim, H., Park, S., Li, Q., Li, X., & Kim, J.
Electronic Commerce Research and Applications, 76, 101586
Multi-Embedding-based Deep Learning Framework for Fake Review Detection in E-commerce Platforms
Lim, H., Shin, J.-H., Choi, J., Li, Q., & Li, J.
Journal of Intelligence and Information Systems, 31(4), 125-146
Enhancing Fake Review Detection with CNN and Linguistic-Semantic Feature Integration
Koo, H., Li, Q., & Lee, S.-K.
Journal of Big Data Service, 3(2), 5-23
A Multimodal Deep Learning Framework for Consistency-Aware Review Helpfulness Prediction
Park, S., Li, X., Li, Q., & Kim, J.
Electronics, 14(15), 3089
A Hybrid Hotel Recommendation Model Leveraging Textual Information to Support Customer Decision-Making
Jang, J., Lee, S. K., & Li, Q.
지능정보연구, 31(2), 61-83
Enhancing Review-Based Recommendations Through Local and Global Feature Fusion
Kim, N., Lim, H., Li, Q., Li, X., Kim, S., & Kim, J.
Electronics, 14(13), 2540
Incorporating Implicit and Explicit Feature Fusion into Hybrid Recommendation for Improved Rating Prediction
Li, Q., Jeong, E., Lee, S. K., & Li, J.
Electronics, 14(12), 2384
A BERT-based review helpfulness prediction model utilizing consistency of ratings and texts
Li, X., Li, Q., Ryu, D., & Kim, J.
Applied Intelligence, 55(7), 455
A BERT-based multi-embedding fusion method using review text for recommendation
Lim, H., Li, Q., Yang, S., & Kim, J.
Expert Systems, 42(5), e70041
ITS-Rec: A Sequential Recommendation Model Using Item Textual Information
Jang, D., Lee, S. K., & Li, Q.
Electronics, 14(9), 1748
Utilizing Advanced Transformers for Summarized Review-Aware Recommender System
Kim, S., Jin, L., Li, Q., Li, X., Yoon, C., & Kim, J.
IEEE Access, 13, 144578-144590
A Multimodal Recommender System Using Deep Learning Techniques Combining Review Texts and Images
Jeong, E., Li, X., Kwon, A. E., Park, S., Li, Q., & Kim, J.
Applied Sciences, 14(20), 9206
A novel deep learning method to use feature complementarity for review helpfulness prediction
Li, X., Li, Q., Jeong, D., & Kim, J.
Journal of Hospitality and Tourism Technology, 15(4), 534-550
AXCF: Aspect‐based collaborative filtering for explainable recommendations
Kim, D., Li, Q., Jang, D., & Kim, J.
Expert Systems, 41(8), e13594
Deep learning mechanism and big data in hospitality and tourism: Developing personalized restaurant recommendation model to customer decision-making
Yang, S., Li, Q., Jang, D., & Kim, J.
International Journal of Hospitality Management, 121, 103803
Attentive Review Semantics-Aware Recommendation Model for Rating Prediction
Kim, J., Li, X., Jin, L., Li, Q., & Kim, J.
Electronics, 13(14), 2815
Empirical comparison of the effects of online and offline recommendation duration on purchasing decisions: case of korea food e-commerce company
Li, Q., Jeong, J., Kim, D., Li, X., Choi, I., & Kim, J.
Asia Pacific Journal of Information Systems, 34(1), 226-247
Attention-based multi attribute matrix factorization for enhanced recommendation performance
Jang, D., Li, Q., Lee, C., & Kim, J.
Information Systems, 121, 102334
Impact of information consistency in online reviews on consumer behavior in the e-commerce industry: a text mining approach
Li, Q., Park, J., & Kim, J.
Data Technologies and Applications, 58(1), 132-149
An attentive aspect-based recommendation model with deep neural network
Yang, S., Li, Q., Lim, H., & Kim, J.
IEEE Access, 12, 5781-5791
A review helpfulness modeling mechanism for online e-commerce: Multi-channel CNN end‑to‑end approach
Li, X., Li, Q., & Kim, J.
Applied Artificial Intelligence, 37(1), 2166226
Restaurant recommendation model using textual information to estimate consumer preference: evidence from an online restaurant platform
Li, Q., Jang, D., Kim, D., & Kim, J.
Journal of Hospitality and Tourism Technology, 14(5), 857-877
Impact on recommendation performance of online review helpfulness and consistency
Park, J., Li, X., Li, Q., & Kim, J.
Data Technologies and Applications, 57(2), 199-221
An empirical investigation of personalized recommendation and reward effect on customer behavior: a stimulus–organism–response (SOR) model perspective
Jeong, J., Kim, D., Li, X., Li, Q., Choi, I., & Kim, J.
Sustainability, 14(22), 15369
역사 맞춤형 정책 수립을 위한 지하철 역사의 유형화 및 특성 분석에 관한 연구: 서울 지하철 사례를 중심으로
김동언, 이청용, 김재경.
인터넷전자상거래연구, 22(4), 153-170
The demand effect analysis of head books and tail books in book recommendation networks
Kim, J. K., Jeong, C. G., Li, Q., & Choi, I. Y.
Expert Systems, 39(2), e12847
A CNN-based advertisement recommendation through real-time user face recognition
Kim, G., Choi, I., Li, Q., & Kim, J.
Applied Sciences, 11(20), 9705
A deep learning-based course recommender system for sustainable development in education
Li, Q., & Kim, J.
Applied Sciences, 11(19), 8993
A hybrid CNN-based review helpfulness filtering model for improving e-commerce recommendation service
Li, Q., Li, X., Lee, B., & Kim, J.
Applied Sciences, 11(18), 8613
Customer satisfaction of recommender system: Examining accuracy and diversity in several types of recommendation approaches
Kim, J., Choi, I., & Li, Q.
Sustainability, 13(11), 6165
리뷰 데이터 마이닝을 이용한 하이브리드 추천시스템 개발: Amazon Kindle Store 데이터 분석사례
장예화, 이청용, 최일영, 김재경.
경영정보학연구, 23(1), 155-172
For the complete and most current record, visit Google Scholar.