| Towards enhancing the last-mile delivery: An effective crowd-tasking model with scalable solutions Y Wang, D Zhang, Q Liu, F Shen, LH Lee Transportation Research Part E: Logistics and Transportation Review 93, 279-293, 2016 | 466 | 2016 |
| PAL: a position-bias aware learning framework for CTR prediction in live recommender systems H Guo, J Yu, Q Liu, R Tang, Y Zhang Proceedings of the 13th ACM Conference on Recommender Systems, 452-456, 2019 | 80 | 2019 |
| An efficient and truthful pricing mechanism for team formation in crowdsourcing markets Q Liu, T Luo, R Tang, S Bressan 2015 IEEE International Conference on Communications (ICC), 567-572, 2015 | 66 | 2015 |
| Personalized re-ranking with item relationships for e-commerce W Liu, Q Liu, R Tang, J Chen, X He, PA Heng Proceedings of the 29th ACM International Conference on Information …, 2020 | 39 | 2020 |
| Cost minimization and social fairness for spatial crowdsourcing tasks Q Liu, T Abdessalem, H Wu, Z Yuan, S Bressan Database Systems for Advanced Applications: 21st International Conference …, 2016 | 30 | 2016 |
| Click-through rate prediction using transfer learning with fine-tuned parameters X Yang, Q Liu, R Su, R Tang, Z Liu, X He, J Yang Information Sciences 612, 188-200, 2022 | 18 | 2022 |
| Context-aware reranking with utility maximization for recommendation Y Xi, W Liu, X Dai, R Tang, W Zhang, Q Liu, X He, Y Yu arXiv preprint arXiv:2110.09059, 2021 | 16 | 2021 |
| U-rank: Utility-oriented learning to rank with implicit feedback X Dai, J Hou, Q Liu, Y Xi, R Tang, W Zhang, X He, J Wang, Y Yu Proceedings of the 29th ACM international conference on information …, 2020 | 13 | 2020 |
| Inter-sequence enhanced framework for personalized sequential recommendation F Liu, W Liu, X Li, Y Ye arXiv preprint arXiv:2004.12118, 2020 | 11 | 2020 |
| Autoft: Automatic fine-tune for parameters transfer learning in click-through rate prediction X Yang, Q Liu, R Su, R Tang, Z Liu, X He arXiv preprint arXiv:2106.04873, 2021 | 10 | 2021 |
| PeerRank: robust learning to rank with peer loss over noisy labels X Wu, Q Liu, J Qin, Y Yu IEEE Access 10, 6830-6841, 2022 | 8 | 2022 |
| Recommendation method and apparatus R Tang, Q Liu, Y Zhang, L Qian, H Chen, W Zhang, Y Yu US Patent App. 17/313,383, 2021 | 6 | 2021 |
| Water Usage in US Unconventional Drilling N Albanese, B Garbaczewski, S Goel, A LaSalle, C Liu, Q Liu, W Utami, ... SIPA Graduate Student Capstone Report Client: Barclays, 1-59, 2016 | 4 | 2016 |
| Beyond relevance ranking: a general graph matching framework for utility-oriented learning to rank X Dai, Y Xi, W Zhang, Q Liu, R Tang, X He, J Hou, J Wang, Y Yu ACM Transactions on Information Systems (TOIS) 40 (2), 1-29, 2021 | 3 | 2021 |
| How to find the best rated items on a Likert scale and how many ratings are enough Q Liu, D Basu, S Goel, T Abdessalem, S Bressan Database and Expert Systems Applications: 28th International Conference …, 2017 | 3 | 2017 |
| Recommendation model training method, selection probability prediction method, and apparatus GUO Huifeng, J Yu, Q Liu, R Tang, X He US Patent App. 17/691,843, 2022 | 2 | 2022 |
| Top-k Queries Over Uncertain Scores Q Liu, D Basu, T Abdessalem, S Bressan On the Move to Meaningful Internet Systems: OTM 2016 Conferences …, 2016 | 2 | 2016 |
| Utility-Oriented Reranking with Counterfactual Context Y Xi, W Liu, X Dai, R Tang, Q Liu, W Zhang, Y Yu ACM Transactions on Knowledge Discovery from Data 18 (8), 1-22, 2024 | 1 | 2024 |
| APSCC 2010 Reviewers E Babulak, S Basu, B Benatallah, J Biswas, S Bressan, R Breu, R Buyya, ... | | |
