| Topicmf: Simultaneously exploiting ratings and reviews for recommendation Y Bao, H Fang, J Zhang Proceedings of the AAAI Conference on Artificial Intelligence 28 (1), 2014 | 372 | 2014 |
| Deep learning for sequential recommendation: Algorithms, influential factors, and evaluations H Fang, D Zhang, Y Shu, G Guo ACM Transactions on Information Systems (TOIS) 39 (1), 1-42, 2020 | 279 | 2020 |
| Research commentary on recommendations with side information: A survey and research directions Z Sun, Q Guo, J Yang, H Fang, G Guo, J Zhang, R Burke Electronic Commerce Research and Applications 37, 100879, 2019 | 218 | 2019 |
| Are we evaluating rigorously? benchmarking recommendation for reproducible evaluation and fair comparison Z Sun, D Yu, H Fang, J Yang, X Qu, J Zhang, C Geng Proceedings of the 14th ACM Conference on Recommender Systems, 23-32, 2020 | 171 | 2020 |
| Towards effective online review systems in the Chinese context: A cross-cultural empirical study H Fang, J Zhang, Y Bao, Q Zhu Electronic Commerce Research and Applications 12 (3), 208-220, 2013 | 130 | 2013 |
| Leveraging decomposed trust in probabilistic matrix factorization for effective recommendation H Fang, Y Bao, J Zhang AAAI 2014, 2014 | 126 | 2014 |
| Multi-faceted trust and distrust prediction for recommender systems H Fang, G Guo, J Zhang Decision Support Systems 71, 37-47, 2015 | 99 | 2015 |
| A blockchain-based scheme for secure data offloading in healthcare with deep reinforcement learning Q He, Z Feng, H Fang, X Wang, L Zhao, Y Yao, K Yu IEEE/ACM Transactions on Networking 32 (1), 65-80, 2023 | 73 | 2023 |
| Dynamic opinion maximization in social networks Q He, H Fang, J Zhang, X Wang IEEE Transactions on Knowledge and Data Engineering 35 (1), 350-361, 2021 | 64 | 2021 |
| Deep learning-based sequential recommender systems: Concepts, algorithms, and evaluations H Fang, G Guo, D Zhang, Y Shu Web Engineering: 19th International Conference, ICWE 2019, Daejeon, South …, 2019 | 64 | 2019 |
| Reputation mechanism for e-commerce in virtual reality environments H Fang, J Zhang, M Şensoy, N Magnenat-Thalmann Electronic Commerce Research and Applications 13 (6), 409-422, 2014 | 51 | 2014 |
| Intrank: Interaction ranking-based trustworthy friend recommendation L Zhang, H Fang, WK Ng, J Zhang 2011IEEE 10th International Conference on Trust, Security and Privacy in …, 2011 | 35 | 2011 |
| A trust model stemmed from the diffusion theory for opinion evaluation H Fang, J Zhang, NM Thalmann Proceedings of the 2013 international conference on autonomous agents and …, 2013 | 33 | 2013 |
| A generalized stereotypical trust model H Fang, J Zhang, M Sensoy, NM Thalmann 2012 IEEE 11th International Conference on Trust, Security and Privacy in …, 2012 | 31 | 2012 |
| Revisiting Bundle Recommendation: Datasets, Tasks, Challenges and Opportunities for Intent-aware Product Bundling Z Sun, J Yang, K Feng, H Fang, X Qu, YS Ong Proceedings of the 45th International ACM SIGIR Conference on Research and …, 2022 | 30 | 2022 |
| Daisyrec 2.0: Benchmarking recommendation for rigorous evaluation Z Sun, H Fang, J Yang, X Qu, H Liu, D Yu, YS Ong, J Zhang IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (7), 8206-8226, 2022 | 29 | 2022 |
| SARC: subjectivity alignment for reputation computation. H Fang, J Zhang, M Sensoy, N Magnenat-Thalmann AAMAS, 1365-1366, 2012 | 27 | 2012 |
| Subjectivity grouping: Learning from users' rating behavior H Fang, J Zhang, N Magnenat Thalmann Proceedings of the 2014 international conference on autonomous agents and …, 2014 | 23 | 2014 |
| Rethinking the Link Prediction Problem in Signed Social Networks X Li, H Fang, J Zhang The Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17), 2017 | 22 | 2017 |
| Multistage Competitive Opinion Maximization With Q-Learning-Based Method in Social Networks Q He, L Zhang, H Fang, X Wang, L Ma, K Yu, J Zhang IEEE Transactions on Neural Networks and Learning Systems, 2024 | 21 | 2024 |
Jie ZhangProfessor of Computer Science, Nanyang Technological UniversityEmail verificata su ntu.edu.sg
