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406 results on '"Dong, Xinzhou"'

7. A Behavior-aware Graph Convolution Network Model for Video Recommendation

Author

Zhuo, Wei, Liu, Kunchi, Xue, Taofeng, Jin, Beihong, Li, Beibei, Dong, Xinzhou, Chen, He, Pan, Wenhai, Zhang, Xuejian, and Zhou, Shuo

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Interactions between users and videos are the major data source of performing video recommendation. Despite lots of existing recommendation methods, user behaviors on videos, which imply the complex relations between users and videos, are still far from being fully explored. In the paper, we present a model named Sagittarius. Sagittarius adopts a graph convolutional neural network to capture the influence between users and videos. In particular, Sagittarius differentiates between different user behaviors by weighting and fuses the semantics of user behaviors into the embeddings of users and videos. Moreover, Sagittarius combines multiple optimization objectives to learn user and video embeddings and then achieves the video recommendation by the learned user and video embeddings. The experimental results on multiple datasets show that Sagittarius outperforms several state-of-the-art models in terms of recall, unique recall and NDCG.

Published
2021

8. Improving Sequential Recommendation with Attribute-augmented Graph Neural Networks

Author

Dong, Xinzhou, Jin, Beihong, Zhuo, Wei, Li, Beibei, and Xue, Taofeng

Subjects
Computer Science - Information Retrieval, Computer Science - Artificial Intelligence
Abstract

Many practical recommender systems provide item recommendation for different users only via mining user-item interactions but totally ignoring the rich attribute information of items that users interact with. In this paper, we propose an attribute-augmented graph neural network model named Murzim. Murzim takes as input the graphs constructed from the user-item interaction sequences and corresponding item attribute sequences. By combining the GNNs with node aggregation and an attention network, Murzim can capture user preference patterns, generate embeddings for user-item interaction sequences, and then generate recommendations through next-item prediction. We conduct extensive experiments on multiple datasets. Experimental results show that Murzim outperforms several state-of-the-art methods in terms of recall and MRR, which illustrates that Murzim can make use of item attribute information to produce better recommendations. At present, Murzim has been deployed in MX Player, one of India's largest streaming platforms, and is recommending videos for tens of thousands of users.

Published
2021

9. Fault Self-healing Scheme of MMC-DC Distribution Line

Author

Liu, Zhong, Zhang, Kaixin, Zhang, Junyan, Shi, Shenxing, Liu, Chaowu, Dong, Xinzhou, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Yadav, Sanjay, editor, Kumar, Rahul, editor, Zainuddin, Hidayat, editor, and Deng, Lvxiang, editor

Published
2023
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10. MARS: A Multi-task Ranking Model for Recommending Micro-videos

Author

Song, Jiageng, Jin, Beihong, Yu, Yisong, Li, Beibei, Dong, Xinzhou, Zhuo, Wei, Zhou, Shuo, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Li, Bohan, editor, Yue, Lin, editor, Tao, Chuanqi, editor, Han, Xuming, editor, Calvanese, Diego, editor, and Amagasa, Toshiyuki, editor

Published
2023
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30. Improved A-STAR Algorithm for Power Line Inspection UAV Path Planning.

Author

Li, Yanchu, Dong, Xinzhou, Ding, Qingqing, Xiong, Yinlong, Liao, Huilian, and Wang, Tao

Subjects
*OPTIMIZATION algorithms, *ELECTRIC lines, *DRONE aircraft, *ALGORITHMS
Abstract

The operational areas for unmanned aerial vehicles (UAVs) used in power line inspection are highly complex; thus, the best path planning under known obstacles is of significant research value for UAVs. This paper establishes a three-dimensional spatial environment based on the gridding and filling of two-dimensional maps, simulates a variety of obstacles, and proposes a new optimization algorithm based on the A-STAR algorithm, considering the unique dynamics and control characteristics of quadcopter UAVs. By utilizing a novel heuristic evaluation function and uniformly applied quadratic B-spline curve smoothing, the planned path is optimized to better suit UAV inspection scenarios. Compared to the traditional A-STAR algorithm, this method offers improved real-time performance and global optimal solution-solving capabilities and is capable of planning safer and more realistic flight paths based on the operational characteristics of quadcopter UAVs in mountainous environments for power line inspection. [ABSTRACT FROM AUTHOR]

Published
2024
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31. List Recommendation via Co-attentive User Preference Fine-Tuning

Author

Li, Beibei, Jin, Beihong, Dong, Xinzhou, Zhuo, Wei, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Mantoro, Teddy, editor, Lee, Minho, editor, Ayu, Media Anugerah, editor, Wong, Kok Wai, editor, and Hidayanto, Achmad Nizar, editor

Published
2021
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32. MULTIPLE: Multi-level User Preference Learning for List Recommendation

Author

Li, Beibei, Jin, Beihong, Dong, Xinzhou, Zhuo, Wei, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Zhang, Wenjie, editor, Zou, Lei, editor, Maamar, Zakaria, editor, and Chen, Lu, editor

Published
2021
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33. Modeling User Profiles Through Multiple Types of User Interaction Behaviors

Author

Lv, Yimin, Dong, Xinzhou, Jin, Beihong, Zhuo, Wei, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Zhang, Wenjie, editor, Zou, Lei, editor, Maamar, Zakaria, editor, and Chen, Lu, editor

Published
2021
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34. Sirius: Sequential Recommendation with Feature Augmented Graph Neural Networks

Author

Dong, Xinzhou, Jin, Beihong, Zhuo, Wei, Li, Beibei, Xue, Taofeng, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Jensen, Christian S., editor, Lim, Ee-Peng, editor, Yang, De-Nian, editor, Lee, Wang-Chien, editor, Tseng, Vincent S., editor, Kalogeraki, Vana, editor, Huang, Jen-Wei, editor, and Shen, Chih-Ya, editor

Published
2021
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35. Feedback-Guided Attributed Graph Embedding for Relevant Video Recommendation

Author

Xue, Taofeng, Dong, Xinzhou, Zhuo, Wei, Jin, Beihong, Chen, He, Pan, Wenhai, Li, Beibei, Zhang, Xuejian, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Dong, Yuxiao, editor, Mladenić, Dunja, editor, and Saunders, Craig, editor

Published
2021
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37. Maturity Analysis of Protection Solutions for Power Systems Near Inverter-Based Resources

Author

Lopes, Felipe V., Davi, Moises J. B. B., Oleskovicz, Mario, Hooshyar, Ali, Dong, Xinzhou, and Neto, Adonias A. A.

Abstract

This review paper studies the literature on protection technologies for power systems near inverter-based resources (IBRs) toward identifying the maturity levels of emerging solutions. Equipment and protection issues that have been investigated in the context of the integration of IBRs are firstly identified, and then, promising emerging protection solutions are assessed regarding attributes like depth of related research, testing experiences using real-world data, availability/feasibility in real protective/control devices and generalization capability. From the obtained results, candidate solutions that may help solving some protection issues that have taken place in systems near IBRs are identified, and challenges that are still to be overcome are highlighted, indicating potential future research and development trends.

Published
2024
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