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Long Xia
Long Xia
Research Scientist, Baidu
在 baidu.com 的电子邮件经过验证
标题
引用次数
引用次数
年份
Deep reinforcement learning for page-wise recommendations
X Zhao, L Xia, L Zhang, Z Ding, D Yin, J Tang
Proceedings of the 12th ACM conference on recommender systems, 95-103, 2018
3912018
Recommendations with negative feedback via pairwise deep reinforcement learning
X Zhao, L Zhang, Z Ding, L Xia, J Tang, D Yin
Proceedings of the 24th ACM SIGKDD international conference on knowledge …, 2018
3772018
Reinforcement learning to optimize long-term user engagement in recommender systems
L Zou, L Xia, Z Ding, J Song, W Liu, D Yin
Proceedings of the 25th ACM SIGKDD international conference on knowledge …, 2019
2392019
Deep reinforcement learning for list-wise recommendations
X Zhao, L Zhang, L Xia, Z Ding, D Yin, J Tang
1st Workshop on Deep Reinforcement Learning for Knowledge Discovery (RL4KD'2019), 2017
1822017
KERL: A knowledge-guided reinforcement learning model for sequential recommendation
P Wang, Y Fan, L Xia, WX Zhao, SZ Niu, J Huang
Proceedings of the 43rd International ACM SIGIR conference on research and …, 2020
1212020
Neural interactive collaborative filtering
L Zou, L Xia, Y Gu, X Zhao, W Liu, JX Huang, D Yin
Proceedings of the 43rd international ACM SIGIR conference on research and …, 2020
1192020
Pseudo Dyna-Q: A reinforcement learning framework for interactive recommendation
L Zou, L Xia, P Du, Z Zhang, T Bai, W Liu, JY Nie, D Yin
Proceedings of the 13th International Conference on Web Search and Data …, 2020
1182020
" Deep reinforcement learning for search, recommendation, and online advertising: a survey" by Xiangyu Zhao, Long Xia, Jiliang Tang, and Dawei Yin with Martin Vesely as coordinator
X Zhao, L Xia, J Tang, D Yin
ACM sigweb newsletter 2019 (Spring), 1-15, 2019
1162019
Adapting Markov decision process for search result diversification
L Xia, J Xu, Y Lan, J Guo, W Zeng, X Cheng
Proceedings of the 40th international ACM SIGIR conference on research and …, 2017
812017
Learning maximal marginal relevance model via directly optimizing diversity evaluation measures
L Xia, J Xu, Y Lan, J Guo, X Cheng
Proceedings of the 38th international ACM SIGIR conference on research and …, 2015
762015
Whole-chain recommendations
X Zhao, L Xia, L Zou, H Liu, D Yin, J Tang
Proceedings of the 29th ACM international conference on information …, 2020
752020
Modeling document novelty with neural tensor network for search result diversification
L Xia, J Xu, Y Lan, J Guo, X Cheng
Proceedings of the 39th International ACM SIGIR conference on Research and …, 2016
622016
Usersim: User simulation via supervised generativeadversarial network
X Zhao, L Xia, L Zou, H Liu, D Yin, J Tang
Proceedings of the Web Conference 2021, 3582-3589, 2021
50*2021
Neural feature-aware recommendation with signed hypergraph convolutional network
X Chen, K Xiong, Y Zhang, L Xia, D Yin, JX Huang
ACM Transactions on Information Systems (TOIS) 39 (1), 1-22, 2020
362020
Reinforcement learning to rank with pairwise policy gradient
J Xu, Z Wei, L Xia, Y Lan, D Yin, X Cheng, JR Wen
Proceedings of the 43rd International ACM SIGIR Conference on Research and …, 2020
352020
Pattern-enhanced Contrastive Policy Learning Network for Sequential Recommendation.
X Tong, P Wang, C Li, L Xia, S Niu
IJCAI, 1593-1599, 2021
302021
Reinforcement learning to diversify top-n recommendation
L Zou, L Xia, Z Ding, D Yin, J Song, W Liu
Database Systems for Advanced Applications: 24th International Conference …, 2019
282019
Directly optimize diversity evaluation measures: A new approach to search result diversification
J Xu, L Xia, Y Lan, J Guo, X Cheng
ACM Transactions on Intelligent Systems and Technology (TIST) 8 (3), 1-26, 2017
262017
Reinforcement recommendation with user multi-aspect preference
X Chen, Y Du, L Xia, J Wang
Proceedings of the Web Conference 2021, 425-435, 2021
182021
Are topics interesting or not? An LDA-based topic-graph probabilistic model for web search personalization
J Zhao, JX Huang, H Deng, Y Chang, L Xia
ACM Transactions on Information Systems (TOIS) 40 (3), 1-24, 2021
122021
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