Runze Wu (吴润泽)

Runze Wu (吴润泽)

Chief Researcher of User Profiling Group, Fuxi AI Lab, NetEase Games

Publications

Balance-Subsampled Stable Prediction Across Unknown Test Data

Published in ACM Transactions on Knowledge Discovery from Data (TKDD), 2021

This paper is about proposing a novel balance-subsampled stable prediction algorithm based on the theory of fractional factorial design to address sample selection bias.

Recommended citation: Kuang, Kun, Hengtao Zhang, Runze Wu, Fei Wu, Yueting Zhuang, and Aijun Zhang. "Balance-Subsampled Stable Prediction Across Unknown Test Data." ACM Transactions on Knowledge Discovery from Data (TKDD) 16, no. 3 (2021): 1-21. https://dl.acm.org/doi/abs/10.1145/3477052

Build Your Own Bundle-A Neural Combinatorial Optimization Method

Published in Proceedings of the 29th ACM International Conference on Multimedia (MM`2021), 2021

This paper is about proposing a novel reinforcement learning based bundle generation methods for recommender systems.

Recommended citation: Deng, Qilin, Kai Wang, Minghao Zhao, Runze Wu*, Yu Ding, Zhene Zou, Yue Shang, Jianrong Tao, and Changjie Fan. "Build Your Own Bundle-A Neural Combinatorial Optimization Method." In Proceedings of the 29th ACM International Conference on Multimedia, pp. 2625-2633. 2021. https://dl.acm.org/doi/abs/10.1145/3474085.3475440

Bilateral filtering graph convolutional network for multi-relational social recommendation in the power-law networks

Published in ACM Transactions on Information Systems (TOIS), 2021

This paper is about proposing a novel bilateral GCN-based method for social recommendation in the long-tail distributed networks.

Recommended citation: Zhao, Minghao, Qilin Deng, Kai Wang, Runze Wu*, Jianrong Tao, Changjie Fan, Liang Chen, and Peng Cui. "Bilateral filtering graph convolutional network for multi-relational social recommendation in the power-law networks." ACM Transactions on Information Systems (TOIS) 40, no. 2 (2021): 1-24. https://dl.acm.org/doi/abs/10.1145/3469799

Globally Optimized Matchmaking in Online Games

Published in Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD`2021), 2021

This paper is about proposing a novel reinforcement learning approach for globally optimizing matchmaking in online games.

Recommended citation: Deng, Qilin, Hao Li, Kai Wang, Zhipeng Hu, Runze Wu*, Linxia Gong, Jianrong Tao, Changjie Fan, and Peng Cui. "Globally Optimized Matchmaking in Online Games." In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 2753-2763. 2021. https://dl.acm.org/doi/abs/10.1145/3447548.3467074

Find Your Organization in MMORPGs

Published in IEEE Transactions on Games, 2021

This paper is about proposing a novel neural model to address guild recommendation in MMORPGs.

Recommended citation: Deng, Qilin, Minghao Zhao, Kai Wang, Runze Wu*, Xudong Shen, Jianrong Tao, and Changjie Fan "Find Your Organization in MMORPGs," in IEEE Transactions on Games, 2021, doi: 10.1109/TG.2021.3104319. https://ieeexplore.ieee.org/abstract/document/9512439/

Reinforcement Learning with a Disentangled Universal Value Function for Item Recommendation

Published in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI`2020), 2021

This paper is about proposing a novel value-based reinforcement learning algorithm for item recommendation.

Recommended citation: Wang, Kai, Zhene Zou, Qilin Deng, Jianrong Tao, Runze Wu*, Changjie Fan, Liang Chen, and Peng Cui. "Reinforcement Learning with a Disentangled Universal Value Function for Item Recommendation." In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 5, pp. 4427-4435. 2021. https://ojs.aaai.org/index.php/AAAI/article/view/16569

NeuralAC: Learning Cooperation and Competition Effects for Match Outcome Prediction

Published in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI`2020), 2021

This paper is about proposing a novel neural model to address cooperation and competition effects in online-game matches.

Recommended citation: Gu, Yin, Qi Liu, Kai Zhang, Zhenya Huang, Runze Wu, and Jianrong Tao. "NeuralAC: Learning Cooperation and Competition Effects for Match Outcome Prediction." In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 5, pp. 4072-4080. 2021. https://ojs.aaai.org/index.php/AAAI/article/view/16528

Prior Aided Streaming Network for Multi-task Affective Analysis

Published in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV`2020), 2021

This paper is about proposing a novel multi-task learning method for affective analysis and wins the First place in ICCV 2021 competition.

Recommended citation: Zhang, Wei, Zunhu Guo, Keyu Chen, Lincheng Li, Zhimeng Zhang, Yu Ding, Runze Wu, Tangjie Lv, and Changjie Fan. "Prior Aided Streaming Network for Multi-task Affective Analysis." In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3539-3549. 2021. https://ieeexplore.ieee.org/abstract/document/9607581

Deep Behavior Tracing with Multi-level Temporality Preserved Embedding

Published in Proceedings of the 29th ACM International Conference on Information & Knowledge Management (CIKM`2020), 2020

This paper is about modeling multiple-level temporality in user behavior sequence to make accurate prediction.

Recommended citation: Wu, Runze, Hao Deng, Jianrong Tao, Changjie Fan, Qi Liu, and Liang Chen. "Deep Behavior Tracing with Multi-level Temporality Preserved Embedding." In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 2813-2820. 2020. https://dl.acm.org/doi/abs/10.1145/3340531.3412696

Match Tracing: A Unified Framework for Real-time Win Prediction and Quantifiable Performance Evaluation

Published in Proceedings of the 29th ACM International Conference on Information & Knowledge Management (CIKM`2020), 2020

This paper is about tracing dynamic winrate in the course of some combat gameplay in online games.

Recommended citation: Wang, Kai, Hao Li, Linxia Gong, Jianrong Tao, Runze Wu, Changjie Fan, Liang Chen, and Peng Cui. "Match Tracing: A Unified Framework for Real-time Win Prediction and Quantifiable Performance Evaluation." In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 2781-2788. 2020. https://dl.acm.org/doi/abs/10.1145/3340531.3412727

Personalized Bundle Recommendation in Online Games

Published in Proceedings of the 29th ACM International Conference on Information & Knowledge Management (CIKM`2020), 2020

This paper is about proposing a novel recommendation method for bundle recommendation in online games.

Recommended citation: Deng, Qilin, Kai Wang, Minghao Zhao, Zhene Zou, Runze Wu, Jianrong Tao, Changjie Fan, and Liang Chen. "Personalized Bundle Recommendation in Online Games." In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 2381-2388. 2020. https://dl.acm.org/doi/abs/10.1145/3340531.3412734

XAI-Driven Explainable Multi-view Game Cheating Detection

Published in 2020 IEEE Conference on Games (CoG`2020), 2020

This paper is about introducing an XAI-based cheat-detection framework in online games and wins the only BEST PAPER Reward in CoG 2020.

Recommended citation: Tao, Jianrong, Yu Xiong, Shiwei Zhao, Yuhong Xu, Jianshi Lin, Runze Wu, and Changjie Fan. "XAI-Driven Explainable Multi-view Game Cheating Detection." In 2020 IEEE Conference on Games (CoG 2020), pp. 144-151. IEEE, 2020. https://ieeexplore.ieee.org/abstract/document/9231843/

Multi-source Data Multi-task Learning for Profiling Players in Online Games

Published in 2020 IEEE Conference on Games (CoG`2020), 2020

This paper is about profiling game players (e.g., churn and payment) by using multi-source data in a multi-task fashion.

Recommended citation: Zhao, Shiwei, Runze Wu, Jianrong Tao, Manhu Qu, Hao Li, and Changjie Fan. "Multi-source Data Multi-task Learning for Profiling Players in Online Games." In 2020 IEEE Conference on Games (CoG 2020), pp. 104-111. IEEE, 2020. https://ieeexplore.ieee.org/abstract/document/9231585/

Optmatch: Optimized matchmaking via modeling the high-order interactions on the arena

Published in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD`2020), 2020

This paper is about making fair matches in online games by using neural optimization methods.

Recommended citation: Gong, Linxia, Xiaochuan Feng, Dezhi Ye, Hao Li, Runze Wu, Jianrong Tao, Changjie Fan, and Peng Cui. "Optmatch: Optimized matchmaking via modeling the high-order interactions on the arena." In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2300-2310. 2020. https://dl.acm.org/doi/10.1145/3394486.3403279

Mvan: Multi-view attention networks for real money trading detection in online games

Published in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD`2019), 2019

This paper is about detecting real money trading in online games.

Recommended citation: Tao, Jianrong, Jianshi Lin, Shize Zhang, Sha Zhao, Runze Wu, Changjie Fan, and Peng Cui. "Mvan: Multi-view attention networks for real money trading detection in online games." In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2536-2546. 2019. https://dl.acm.org/doi/abs/10.1145/3292500.3330687

Confidence-aware matrix factorization for recommender systems

Published in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI`2018), 2018

This paper is about modeling the confidence of recommender system by using the matrix factorization methods.

Recommended citation: Wang, Chao, Qi Liu, Runze Wu, Enhong Chen, Chuanren Liu, Xunpeng Huang, and Zhenya Huang. "Confidence-aware matrix factorization for recommender systems." In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, no. 1. 2018. https://ojs.aaai.org/index.php/AAAI/article/download/11251/11110

Tracking knowledge proficiency of students with educational priors

Published in Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (CIKM`2017), 2017

This paper is about tracking student knowledge proficiency with educational priors.

Recommended citation: Chen, Yuying, Qi Liu, Zhenya Huang, Le Wu, Enhong Chen, Runze Wu, Yu Su, and Guoping Hu. "Tracking knowledge proficiency of students with educational priors." In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 989-998. 2017. https://dl.acm.org/doi/abs/10.1145/3132847.3132929

Knowledge or gaming? Cognitive modelling based on multiple-attempt response

Published in Proceedings of the 26th International Conference on World Wide Web Companion (WWW`2017), 2017

This paper is about cognitively modeling K-12 students in multiple-response settings.

Recommended citation: Wu, Runze, Guandong Xu, Enhong Chen, Qi Liu, and Wan Ng. "Knowledge or gaming? Cognitive modelling based on multiple-attempt response." In Proceedings of the 26th International Conference on World Wide Web Companion, pp. 321-329. 2017. https://dl.acm.org/doi/abs/10.1145/3041021.3054156

Collaborative learning team formation: a cognitive modeling perspective

Published in International Conference on Database Systems for Advanced Applications (DASFAA`2016), 2016

This paper is about setting up learning teams for better collaborative learning.

Recommended citation: Liu, Yuping, Qi Liu, Runze Wu, Enhong Chen, Yu Su, Zhigang Chen, and Guoping Hu. "Collaborative learning team formation: a cognitive modeling perspective." In International Conference on Database Systems for Advanced Applications, pp. 383-400. Springer, Cham, 2016. https://link.springer.com/chapter/10.1007/978-3-319-32049-6_24

Cognitive Modelling for Predicting Examinee Performance

Published in Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI`2015), 2015

This paper is about cognitive modeling for K-12 students.

Recommended citation: Wu, Runze, Qi Liu, Yuping Liu, Enhong Chen, Yu Su, Zhigang Chen, and Guoping Hu. (2015) "Cognitive modelling for predicting examinee performance." In Twenty-Fourth International Joint Conference on Artificial Intelligence. https://www.aaai.org/ocs/index.php/IJCAI/IJCAI15/paper/download/11121/10804