Zobrazeno 1 - 10
of 299
pro vyhledávání: '"Wu, Lixia"'
In addressing the pivotal role of translating natural language queries into SQL commands, we propose a suite of compact, fine-tuned models and self-refine mechanisms to democratize data access and analysis for non-expert users, mitigating risks assoc
Externí odkaz:
http://arxiv.org/abs/2409.15985
Real-world scenarios frequently involve multi-objective data-driven optimization problems, characterized by unknown problem coefficients and multiple conflicting objectives. Traditional two-stage methods independently apply a machine learning model t
Externí odkaz:
http://arxiv.org/abs/2406.00779
Autor:
Lu, Minfang, Jiang, Yuchen, Dong, Huihui, Li, Qi, Xu, Ziru, Liu, Yuanlin, Wu, Lixia, Hu, Haoyuan, Zhu, Han, Jiang, Yuning, Xu, Jian, Zheng, Bo
In large-scale industrial e-commerce, the efficiency of an online recommendation system is crucial in delivering highly relevant item/content advertising that caters to diverse business scenarios. However, most existing studies focus solely on item a
Externí odkaz:
http://arxiv.org/abs/2310.15492
A Survey on Service Route and Time Prediction in Instant Delivery: Taxonomy, Progress, and Prospects
Autor:
Wen, Haomin, Lin, Youfang, Wu, Lixia, Mao, Xiaowei, Cai, Tianyue, Hou, Yunfeng, Guo, Shengnan, Liang, Yuxuan, Jin, Guangyin, Zhao, Yiji, Zimmermann, Roger, Ye, Jieping, Wan, Huaiyu
Instant delivery services, such as food delivery and package delivery, have achieved explosive growth in recent years by providing customers with daily-life convenience. An emerging research area within these services is service Route\&Time Predictio
Externí odkaz:
http://arxiv.org/abs/2309.01194
Autor:
Mao, Xiaowei, Wen, Haomin, Zhang, Hengrui, Wan, Huaiyu, Wu, Lixia, Zheng, Jianbin, Hu, Haoyuan, Lin, Youfang
Pick-up and Delivery Route Prediction (PDRP), which aims to estimate the future service route of a worker given his current task pool, has received rising attention in recent years. Deep neural networks based on supervised learning have emerged as th
Externí odkaz:
http://arxiv.org/abs/2307.16246
Autor:
Wu, Lixia, Wen, Haomin, Hu, Haoyuan, Mao, Xiaowei, Xia, Yutong, Shan, Ergang, Zhen, Jianbin, Lou, Junhong, Liang, Yuxuan, Yang, Liuqing, Zimmermann, Roger, Lin, Youfang, Wan, Huaiyu
Real-world last-mile delivery datasets are crucial for research in logistics, supply chain management, and spatio-temporal data mining. Despite a plethora of algorithms developed to date, no widely accepted, publicly available last-mile delivery data
Externí odkaz:
http://arxiv.org/abs/2306.10675
Autor:
Wu, Lixia, Liu, Jianlin, Lou, Junhong, Hu, Haoyuan, Zheng, Jianbin, Wen, Haomin, Song, Chao, He, Shu
Text-based delivery addresses, as the data foundation for logistics systems, contain abundant and crucial location information. How to effectively encode the delivery address is a core task to boost the performance of downstream tasks in the logistic
Externí odkaz:
http://arxiv.org/abs/2304.01559
Autor:
Chen, Dingyu1,2 (AUTHOR), Wu, Lixia1 (AUTHOR), Liu, Xi1 (AUTHOR), Wang, Qinrong1 (AUTHOR), Gui, Shuqin1 (AUTHOR), Bao, Liya3 (AUTHOR), Wang, Zhengrong4 (AUTHOR), He, Xiaofeng1 (AUTHOR), Zhao, Yan1 (AUTHOR) 501155185@qq.com, Zhou, Jianjiang1 (AUTHOR) jianjiangzhou@sina.cn, Xie, Yuan1 (AUTHOR) 37408126@qq.com
Publikováno v:
Scientific Reports. 9/17/2024, Vol. 14 Issue 1, p1-14. 14p.
Large-scale commercial platforms usually involve numerous business domains for diverse business strategies and expect their recommendation systems to provide click-through rate (CTR) predictions for multiple domains simultaneously. Existing promising
Externí odkaz:
http://arxiv.org/abs/2211.12105
Autor:
Li, Fangjian, Mai, Cuishan, Liu, Yan, Deng, Yaru, Wu, Lixia, Zheng, Xinni, He, Huijing, Huang, Yilin, Luo, Zhenxi, Wang, Jinxiang
Publikováno v:
In Plant Science November 2024 348