A deep relevance matching model for ad-hoc retrieval J Guo, Y Fan, Q Ai, WB Croft Proceedings of the 25th ACM international on conference on information and …, 2016 | 965 | 2016 |
Learning heterogeneous knowledge base embeddings for explainable recommendation Q Ai, V Azizi, X Chen, Y Zhang Algorithms 11 (9), 137, 2018 | 424 | 2018 |
Towards conversational search and recommendation: System ask, user respond Y Zhang, X Chen, Q Ai, L Yang, WB Croft Proceedings of the 27th acm international conference on information and …, 2018 | 403 | 2018 |
A deep look into neural ranking models for information retrieval J Guo, Y Fan, L Pang, L Yang, Q Ai, H Zamani, C Wu, WB Croft, X Cheng Information Processing & Management 57 (6), 102067, 2020 | 370 | 2020 |
Joint representation learning for top-n recommendation with heterogeneous information sources Y Zhang, Q Ai, X Chen, WB Croft Proceedings of the 2017 ACM on Conference on Information and Knowledge …, 2017 | 332 | 2017 |
aNMM: Ranking short answer texts with attention-based neural matching model L Yang, Q Ai, J Guo, WB Croft Proceedings of the 25th ACM international on conference on information and …, 2016 | 233 | 2016 |
Unbiased learning to rank with unbiased propensity estimation Q Ai, K Bi, C Luo, J Guo, WB Croft The 41st international ACM SIGIR conference on research & development in …, 2018 | 224 | 2018 |
Learning a deep listwise context model for ranking refinement Q Ai, K Bi, J Guo, WB Croft The 41st international ACM SIGIR conference on research & development in …, 2018 | 209 | 2018 |
Learning a hierarchical embedding model for personalized product search Q Ai, Y Zhang, K Bi, X Chen, WB Croft Proceedings of the 40th International ACM SIGIR Conference on Research and …, 2017 | 133 | 2017 |
Learning over knowledge-base embeddings for recommendation Y Zhang, Q Ai, X Chen, P Wang arXiv preprint arXiv:1803.06540, 2018 | 124 | 2018 |
Personalized key frame recommendation X Chen, Y Zhang, Q Ai, H Xu, J Yan, Z Qin Proceedings of the 40th international ACM SIGIR conference on research and …, 2017 | 116 | 2017 |
Setrank: Learning a permutation-invariant ranking model for information retrieval L Pang, J Xu, Q Ai, Y Lan, X Cheng, J Wen Proceedings of the 43rd international ACM SIGIR conference on research and …, 2020 | 113 | 2020 |
Analysis of the paragraph vector model for information retrieval Q Ai, L Yang, J Guo, WB Croft Proceedings of the 2016 ACM international conference on the theory of …, 2016 | 108 | 2016 |
Learning groupwise multivariate scoring functions using deep neural networks Q Ai, X Wang, S Bruch, N Golbandi, M Bendersky, M Najork Proceedings of the 2019 ACM SIGIR international conference on theory of …, 2019 | 101 | 2019 |
Semantic matching by non-linear word transportation for information retrieval J Guo, Y Fan, Q Ai, WB Croft Proceedings of the 25th ACM International on Conference on Information and …, 2016 | 89 | 2016 |
Beyond factoid QA: effective methods for non-factoid answer sentence retrieval L Yang, Q Ai, D Spina, RC Chen, L Pang, WB Croft, J Guo, F Scholer Advances in Information Retrieval: 38th European Conference on IR Research …, 2016 | 88 | 2016 |
Conversational product search based on negative feedback K Bi, Q Ai, Y Zhang, WB Croft Proceedings of the 28th acm international conference on information and …, 2019 | 67 | 2019 |
Unbiased learning to rank: online or offline? Q Ai, T Yang, H Wang, J Mao ACM Transactions on Information Systems (TOIS) 39 (2), 1-29, 2021 | 63 | 2021 |
A zero attention model for personalized product search Q Ai, DN Hill, SVN Vishwanathan, WB Croft Proceedings of the 28th ACM International Conference on Information and …, 2019 | 63 | 2019 |
Explainable product search with a dynamic relation embedding model Q Ai, Y Zhang, K Bi, WB Croft ACM Transactions on Information Systems (TOIS) 38 (1), 1-29, 2019 | 56 | 2019 |