Long Xia is a research scientist in Data Science Lab at JD.com. He received his Ph.D. in Computer Science from Institute of Computing Technology, Chinese Academy of Sciences. His research interests include information retrieval, machine learning, and data mining.
Li He, Long Xia, Wei Zeng, Zhi-Ming Ma, Yihong Zhao, and Dawei Yin. Off-policy Learning for Multiple Loggers. In SIGKDD'19, Anchorage, Alaska, USA, 2019.
Lixin Zou, Long Xia, Zhuoye Ding, Jiaxing Song, Weidong Liu, and Dawei Yin. Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems. In SIGKDD'19, Anchorage, Alaska, USA, 2019.
Lixin Zou, Long Xia, Zhuoye Ding, Dawei Yin, Jiaxing Song, and Weidong Liu. Reinforcement Learning to Diversify Top-N Recommendation. In DASFAA'19, Chiang Mai, Thailand, April 2019.
Xiangyu Zhao, Liang Zhang, Zhuoye Ding, Long Xia, Jiliang Tang, and Dawei Yin. Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning. In SIGKDD 2018, London, United Kingdom, 2018.
Xiangyu Zhao, Long Xia, Liang Zhang, Zhuoye Ding, Dawei Yin, and Jiliang Tang. Deep Reinforcement Learning for Page-wise Recommendations. In RecSys 2018, Vancouver, Canada, 2018.
Long Xia, Jun Xu, Yanyan Lan, Jiafeng Guo, Wei Zeng, and Xueqi Cheng. Adapting Markov decision process for search result diversification. In SIGIR ’17, Tokyo, 2017.
Jun Xu, Long Xia, Yanyan Lan, Jiafeng Guo, and Xueqi Cheng. Directly optimize diversity evaluation measures: A new approach to search result diversification. ACM Trans. Intell. Syst. Technol. 8, 3, Article 41 (Jan. 2017), 26 pages.
Long Xia, Jun Xu, Yanyan Lan, Jiafeng guo, and Xueqi Cheng. Modeling document novelty with neural tensor network for search result diversification. In SIGIR ’16, Pisa, 2016.
Long Xia, Jun Xu, Yanyan Lan, Jiafeng Guo, and Xueqi Cheng. Learning maximal marginal relevance model via directly optimizing diversity evaluation measures. In SIGIR ’15, Santiago de Chile, 2015.