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pro vyhledávání: '"Liang, Yile"'
Autor:
Liang, Yile, Zhao, Jiuxia, Li, Donghui, Feng, Jie, Zhang, Chen, Ding, Xuetao, Hao, Jinghua, He, Renqing
The recent past has witnessed a notable surge in on-demand food delivery (OFD) services, offering delivery fulfillment within dozens of minutes after an order is placed. In OFD, pooling multiple orders for simultaneous delivery in real-time order ass
Externí odkaz:
http://arxiv.org/abs/2406.14635
Autor:
Zhao, Minghao, Wu, Le, Liang, Yile, Chen, Lei, Zhang, Jian, Deng, Qilin, Wang, Kai, Shen, Xudong, Lv, Tangjie, Wu, Runze
Recent years have witnessed the great accuracy performance of graph-based Collaborative Filtering (CF) models for recommender systems. By taking the user-item interaction behavior as a graph, these graph-based CF models borrow the success of Graph Ne
Externí odkaz:
http://arxiv.org/abs/2204.12326
Autor:
Wang, Kai, Zou, Zhene, Zhao, Minghao, Deng, Qilin, Shang, Yue, Liang, Yile, Wu, Runze, Shen, Xudong, Lyu, Tangjie, Fan, Changjie
Reinforcement learning based recommender systems (RL-based RS) aim at learning a good policy from a batch of collected data, by casting recommendations to multi-step decision-making tasks. However, current RL-based RS research commonly has a large re
Externí odkaz:
http://arxiv.org/abs/2110.11073
One key property in recommender systems is the long-tail distribution in user-item interactions where most items only have few user feedback. Improving the recommendation of tail items can promote novelty and bring positive effects to both users and
Externí odkaz:
http://arxiv.org/abs/2106.14388
Autor:
Liang, Yile, Qian, Tieyun
Recommender systems have played a vital role in online platforms due to the ability of incorporating users' personal tastes. Beyond accuracy, diversity has been recognized as a key factor in recommendation to broaden user's horizons as well as to pro
Externí odkaz:
http://arxiv.org/abs/2101.00781
Matrix completion is a classic problem underlying recommender systems. It is traditionally tackled with matrix factorization. Recently, deep learning based methods, especially graph neural networks, have made impressive progress on this problem. Desp
Externí odkaz:
http://arxiv.org/abs/1912.12398
Autor:
Zheng, Jie, Wang, Ling, Chen, Jing-fang, Wang, Xing, Liang, Yile, Duan, Haining, Li, Zixuan, Ding, Xuetao
Publikováno v:
In Computers & Industrial Engineering October 2022 172 Part A
Akademický článek
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This paper proposes a convex optimization based distributed algorithm to solve multi-period optimal gas-power flow (OGPF) in coupled energy distribution systems. At the gas distribution system side, the non-convex Weymouth gas flow equations is conve
Externí odkaz:
http://arxiv.org/abs/1610.04681
Publikováno v:
In Neurocomputing 25 August 2020 403:337-347