SlateQ: A tractable decomposition for reinforcement learning with recommendation sets E Ie, V Jain, J Wang, S Narvekar, R Agarwal, R Wu, HT Cheng, T Chandra, ... | 149 | 2019 |
Reinforcement learning for slate-based recommender systems: A tractable decomposition and practical methodology E Ie, V Jain, J Wang, S Narvekar, R Agarwal, R Wu, HT Cheng, ... arXiv preprint arXiv:1905.12767, 2019 | 75 | 2019 |
Zero-Shot Heterogeneous Transfer Learning from Recommender Systems to Cold-Start Search Retrieval T Wu, EKI Chio, HT Cheng, Y Du, S Rendle, D Kuzmin, R Agarwal, ... Proceedings of the 29th ACM International Conference on Information …, 2020 | 26 | 2020 |
“I know what you feel”: analyzing the role of conjunctions in automatic sentiment analysis R Agarwal, TV Prabhakar, S Chakrabarty Advances in Natural Language Processing: 6th International Conference, GoTAL …, 2008 | 17 | 2008 |
Modeling Information Need of Users in Search Sessions K Halder, HT Cheng, EKI Chio, G Roumpos, T Wu, R Agarwal arXiv preprint arXiv:2001.00861, 2020 | 4 | 2020 |
Keyword bid optimization under cost per click constraints PSR Pavagada, H Prakash, R Agarwal, V Ramaiyer US Patent App. 12/702,690, 2011 | 2 | 2011 |
Reinforcement learning in combinatorial action spaces TWE Ie, J Vihan, J Wang, R Agarwal, CE Boutilier US Patent App. 16/975,060, 2021 | 1 | 2021 |
Zero-Shot Transfer Learning for Query-Item Cold Start in Search Retrieval and Recommendations A Kumar, C Du, D Kuzmin, EH Chi, HT Cheng, JR Anderson, L Zhang, ... | | 2020 |