Preference and Trust, Recommender System, User Modeling

Huizhong Guo, Dongxia Wang, Zhu Sun, Haonan Zhang, Jinfeng Li and Jie Zhang, Configurable Fairness for New Item Recommendation Considering Entry Time of Items, 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2024 (20.1% of acceptance, 159/1148)

Yingpeng Du, Di Luo, Rui Yan, Xiaopei Wang, Hongzhi Liu, Hengshu Zhu, Yang Song and Jie Zhang, Enhancing Job Recommendation through LLM-based Generative Adversarial Networks, Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI), 2024 (23.75% of acceptance, 2342/12100)

Youchen Sun, Zhu Sun, Xiao Sha, Jie Zhang and Yew Soon Ong, Disentangling Motives behind Item Consumption and Social Connection for Mutually-enhanced Joint Prediction, 17th ACM Recommender Systems Conference (RecSys), 2023 (18.7% of acceptance, 47/251)

Yuxin Ni, Yunwen Xia, Hui Fang, Chong Long, Xinyu Kong, Daqian Li, Yang Dong and Jie Zhang, Meta-CRS: A Dynamic Meta-Learning Approach for Effective Conversational Recommender System, ACM Transactions on Information Systems (TOIS), accepted, 2023 (Impact Factor: 4.797)

Zhu Sun, Hui Fang, Jie Yang, Xinghua Qu, Hongyang Liu, Di Yu, Yew-Soon Ong and Jie Zhang, DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), accepted, 2022 (Impact Factor: 24.31)

Xin Zhou, Aixin Sun, Yong Liu, Jie Zhang and Chunyan Miao, SelfCF: A Simple Framework for Self-supervised Collaborative Filtering, ACM Transactions on Recommender Systems (TORS), accepted, 2023

Zhu Sun, Yu Lei, Lu Zhang, Chen Li, Yew-Soon Ong and Jie Zhang, A Multi-Channel Next POI Recommendation Framework with Multi-Granularity Check-in Signals, ACM Transactions on Information Systems (TOIS), accepted, 2023 (Impact Factor: 4.797)

Kaixin Wang, Cheng Long, Da Yan, Jie Zhang and H. V. Jagadish, Reinforcement Learning Enhanced Weighted Sampling for Accurate Subgraph Counting on Fully Dynamic Graph Streams, 39th IEEE International Conference on Data Engineering (ICDE), 2023

Xiao Sha, Zhu Sun, Jie Zhang and Yew Soon Ong, Who Wants to Shop with You: Joint Product-Participant Recommendation for Group-Buying Service, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), accepted, 2022 (Impact Factor: 14.26)

Yitong Ji, Aixin Sun, Jie Zhang and Chenliang Li, A Critical Study on Data Leakage in Recommender System Offline Evaluation, ACM Transactions on Information Systems (TOIS), accepted, 2022 (Impact Factor: 4.797)

Qidong Liu, Cheng Long, Jie Zhang, Mingliang Xu and Dacheng Tao, Aspect-Aware Graph Attention Network for Heterogeneous Information Networks, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), accepted, 2022 (Impact Factor: 14.26)

Shen Xin, Yuhang Jiao, Yuguang Wang, Cheng Long, Xiaowei Wang, Sen Yang, Ji Liu and Jie Zhang, Prototype Feature Extraction for Multi-task Learning, The ACM Web Conference (WWW), 2022 (17.7% of acceptance, 323/1822)

Lu Zhang, Zhu Sun, Ziqing Wu, Jie Zhang, Yew Soon Ong and Xinghua Qu, Next Point-of-Interest Recommendation with Inferring Multi-step Future Preferences, The 31st International Joint Conference on Artificial Intelligence (IJCAI), 2022 (15% of acceptance out of 4535 submissions)

Mingsheng Fu, Liwei Huang, Ananya Yao, Athirai Irissappane, Jie Zhang and Hong Qu, A Deep Reinforcement Learning Recommender system with Multiple Policies for Recommendations, IEEE Transactions on Industrial Informatics, accepted, 2022 (Impact Factor: 10.215)

Lu Zhang, Zhu Sun, Jie Zhang, Yiwen Wu and Yunwen Xia, Conversation-based Adaptive Relational Translation Method for Next POI Recommendation with Uncertain Check-ins, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), accepted, 2022 (Impact Factor: 8.793)

Xiao Sha, Zhu Sun and Jie Zhang, Disentangling Multi-facet Social Relations for Recommendation, IEEE Transactions on Computational Social Systems, accepted, 2021 (Impact Factor: 5.36)

Danni Peng, Sinno Jialin Pan, Jie Zhang and Anxiang Zeng, Learning An Adaptive Meta Model-Generator for Incrementally Updating Recommender Systems, 15th ACM Conference on Recommender Systems (RecSys), 2021 (18.4% of acceptance, 49/267)

Xiao Sha, Zhu Sun and Jie Zhang, Hierarchical Attentive Knowledge Graph Embedding for Personalized Recommendation, Electronic Commerce Research and Applications, 2021 (Impact Factor: 6.014)

Mingsheng Fu, Anubha Agrawal, Athirai A. Irissappane, Jie Zhang, Liwei Huang and Hong Qu, Deep Reinforcement Learning Framework for Category Based Item Recommendation, IEEE Transactions on Cybernetics, 2021(Impact Factor: 15.84)

Qidong Liu, Cheng Long, Jie Zhang, Mingliang Xu and Pei Lv, TriATNE: Tripartite Adversarial Training for Network Embeddings, IEEE Transactions on Cybernetics, 2021 (Impact Factor: 15.84)

Shen Xin, Zhao Li, Pengcheng Zou, Cheng Long, Jie Zhang, Jiajun Bu and Jingren Zhou, ATNN: Adversarial Two-Tower Neural Network for New Item's Popularity Prediction in E-commerce, IEEE International Conference on Data Engineering (ICDE) Industry and Applications Track, 2021

Zhu Sun, Li Chen, Lei Yu, Lu Zhang, Jie Zhang and Liangshun Pan, Point-of-Interest Recommendation for Users-Businesses with Uncertain Check-ins, IEEEE Transactions on Knowledge and Data Engineering, 2021 (Impact Factor: 4.935)

Lu Zhang, Zhu Sun, Jie Zhang, Yu Lei, Chen Li and Ziqing Wu, An Interactive Multi-Task Learning Framework for Next POI Recommendation with Uncertain Check-ins, 29th International Joint Conference on Artificial Intelligence (IJCAI), 2020 (12.6% of acceptance, 592/4717)

Qing Guo, Zhu Sun, Jie Zhang and Yin-Leng Theng, An Attentional Recurrent Neural Network for Personalized Next Location Recommendation, Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), 2020 (20.6% of acceptance, 1591/7737)

Yankai Chen, Jie Zhang, Yixiang Fang, Xin Cao and Irwin King, Efficient Community Search over Large Directed Graph: An Augmented Index-based Approach, 29th International Joint Conference on Artificial Intelligence (IJCAI), 2020 (12.6% of acceptance, 592/4717)

Zhu Sun, Di Yu, Hui Fang, Jie Yang, Xinghua Qu, Jie Zhang and Cong Geng, Are We Evaluating Rigorously? Benchmarking Recommendation for Reproducible Evaluation and Fair Comparison, 14th ACM Conference on Recommender Systems (RecSys), Reproducibility track, 2020 (18% of acceptance, 39/218)

Shen Xin, Yizhou Ye, Martin Ester, Cheng Long, Jie Zhang, Zhao Li, Kaiying Yuan and Yanghua Li, Multi-Channel Sellers Traffic Allocation in Large-scale E-commerce Promotion, 29th ACM International Conference on Information and Knowledge Management (CIKM) Applied Research Track, 2020

Qidong Liu, Xin Zhou, Cheng Long, Jie Zhang and Mingliang Xu, Learning network representations with different order structural information, IEEE Transactions on Computational Social Systems (TCSS), accepted, 2020

Lu Zhang, Zhu Sun, Jie Zhang, Horst Kloeden and Felix Klanner, Modeling Hierarchical Category Transition for Next POI Recommendation with Uncertain Check-ins, Information Sciences, accepted, 2019 (Impact Factor: 5.524)

Zhu Sun, Qing Guo, Jie Yang, Hui Fang, Guibing Guo, Jie Zhang and Robin Burke, Research Commentary on Recommendations with Side Information: A Survey and Research Directions, Electronic Commerce Research and Applications, accepted, 2019 (5-Year Impact Factor: 3.661)

Zhu Sun, Jie Yang, Jie Zhang, Alessandro Bozzon, Longkai Huang and Chi Xu, Recurrent Knowledge Graph Embedding for Effective Recommendation, 12th ACM Conference on Recommender Systems (RecSys), 2018 (17.7% of acceptance)

Huihuai Qiu, Yun Liu, Guibing Guo, Zhu Sun, Jie Zhang and Hai Thanh Nguyen, BPRH: Bayesian Personalized Ranking for Heterogeneous Implicit Feedback, Information Sciences, accepted, 2018 (Impact Factor: 4.832)

Yankai Chen, Yixiang Fang, Reynold Cheng, Yun Li, Xiaojun Chen and Jie Zhang, Exploring Communities in Large Profiled Graphs, IEEE Transactions on Knowledge and Data Engineering (TKDE), 31(8), pp. 1624-1629, 2018 (Impact Factor: 3.857)

Zhu Sun, Jie Yang, Jie Zhang, Alessandro Bozzon and Chi Xu, MRLR: Multi-level Representation Learning for Personalized Ranking in Recommendation, 26th International Joint Conference on Artificial Intelligence (IJCAI), 2017 (26% of acceptance, 660/2540)

Zhu Sun, Jie Yang, Jie Zhang and Alessandro Bozzon, Exploiting both Vertical and Horizontal Dimensions of Feature Hierarchy for Effective Recommendation, 31st AAAI Conference on Artificial Intelligence (AAAI), 2017 (24.6% of acceptance, 638/2590)

Wenjie Pei, Jie Yang, Zhu Sun, Jie Zhang, Alessandro Bozzon and David Tax, Interactive Attention-Gated Recurrent Networks for Recommendation, 26th ACM International Conference on Information and Knowledge Management (CIKM), 2017 (21% of acceptance, 171/820)

Guibing Guo, Jie Zhang, Feida Zhu and Xingwei Wang, Factored Similarity Models with Social Trust for Top-N Item Recommendation, Knowledge-Based Systems, accepted, 2017 (Impact Factor: 3.325)

Hongbing Wang, Bin Zou, Guibing Guo, Danrong Yang and Jie Zhang, Integrating Trust with User Preference for Effective Web Service Composition, IEEE Transactions on Services Computing, 10(4), pp. 574-588, 2017 (Impact Factor: 5.707, conference version: Optimal and Effective Web Service Composition with Trust and User Preference, 22nd IEEE International Conference on Web Services (ICWS), 2015 (20% of acceptance))

Jie Yang, Zhu Sun, Alessandro Bozzon and Jie Zhang, Learning Hierarchical Feature Influence for Recommendation by Recursive Regularization, 10th ACM Conference on Recommender Systems (RecSys), 2016 (18.2% of acceptance)

Guibing Guo, Jie Zhang and Neil Yorke-Smith, A Novel Recommendation Model Regularized with User Trust and Item Ratings, IEEE Transactions on Knowledge and Data Engineering (TKDE), accepted, 2016 (Impact Factor: 2.067, conference version: TrustSVD: Collaborative Filtering with Both the Explicit and Implicit Influence of User Trust and of Item Ratings, Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI), 2015 (26.67% of acceptance, 531/1991))

Guibing Guo, Jie Zhang and Neil Yorke-Smith, A Novel Evidence-based Bayesian Similarity Measure for Recommender Systems, ACM Transactions on the Web, in press, 2016 (Impact Factor: 1.595, conference version: A Novel Bayesian Similarity Measure for Recommender Systems, 23rd International Joint Conference on Artificial Intelligence (IJCAI), 2013 (28% of acceptance, 413/1473))

Hui Fang, Guibing Guo and Jie Zhang, Multi-Faceted Trust and Distrust Prediction for Recommender Systems, Decision Support Systems, 71, pp. 37-47, 2015 (Impact Factor: 2.036)

Guibing Guo, Jie Zhang and Neil Yorke-Smith, Leveraging Multiviews of Trust and Similarity to Enhance Clustering-based Recommender Systems, Knowledge-Based Systems, Volume 74, pp. 14-27, 2015 (Impact Factor: 4.104)

Yang Bao, Hui Fang and Jie Zhang, TopicMF: Simultaneously Exploiting Ratings and Reviews for Recommendation, Twenty-Eighth AAAI Conference on Artificial Intelligence (AAAI), 2014 (28% of acceptance, 398/1406)

Hui Fang, Yang Bao and Jie Zhang, Leveraging Decomposed Trust in Probabilistic Matrix Factorization for Effective Recommendation, Twenty-Eighth AAAI Conference on Artificial Intelligence (AAAI), 2014 (28% of acceptance, 398/1406)

Zhu Sun, Guibing Guo and Jie Zhang, Exploiting Implicit Item Relationships for Recommender Systems, 22nd International Conference on User Modeling, Adaptation and Personalization (UMAP), 2015 (28% of acceptance)

Guibing Guo, Jie Zhang, Daniel Thalmann and Neil Yorke-Smith, Leveraging Prior Ratings for Recommender Systems in E-Commerce, Electronic Commerce Research and Applications (ECRA), 13(6), pp. 440-455, 2014 (Impact Factor: 2.139, conference version: Prior Ratings: A New Information Source for Recommender Systems in E-Commerce, ACM Recommender System conference (RecSys), 2013)

Guibing Guo, Jie Zhang and Daniel Thalmann, Merging Trust in Collaborative Filtering to Alleviate Data Sparsity and Cold Start, Knowledge-Based Systems, 57, pp. 57-68, 2014 (Impact Factor: 4.104, conference version: A Simple but Effective Method to Incorporate Trusted Neighbors in Recommender Systems, 20th International Conference on User Modeling, Adaptation and Personalization (UMAP), 2012 (21.7% of acceptance))

Aaditeshwar Seth, Jie Zhang and Robin Cohen, A Personalized Credibility Model for Recommending Messages in Social Participatory Media Environments, World Wide Web, 18(1), pp. 111-137, 2015 (Impact Factor: 1.623, conference version: Bayesian Credibility Modeling for Personalized Recommendation in Participatory Media, International Conference on User Modeling, Adaptation and Personalization (UMAP), 2010 (20% of acceptance))

Jie Zhang, Yuan Wang and Julita Vassileva, SocConnect: A Personalized Social Network Aggregator and Recommender, Information Processing & Management (IPM), 49(3), pp. 721-737, 2013 (5-Year Impact Factor: 1.443)

John Champaign, Jie Zhang and Robin Cohen, Coping with Poor Advice from Peers in Peer-Based Intelligent Tutoring: The Case of Avoiding Bad Annotations of Learning Objects, International Conference on User Modeling, Adaptation and Personalization (UMAP), 2011 (20% of acceptance)

Aaditeshwar Seth and Jie Zhang, A Social Network Based Approach to Personalized Recommendation of Participatory Media Content, International AAAI Conference on Weblogs and Social Media (ICWSM), 2008 (25% of acceptance, 20/80)

Machine Learning and Optimization

Cong Zhang, Zhiguang Cao, Wen Song, Yaoxin Wu and Jie Zhang, Deep Reinforcement Learning Guided Improvement Heuristic for Job Shop Scheduling, Twelfth International Conference on Learning Representations (ICLR), 2024 (31% of acceptance out of 7262 submissions)

Jiacheng Chen, Zeyuan Ma, Hongshu Guo, Yining Ma, Jie Zhang and Yue-Jiao Gong, SYMBOL: Generating Flexible Black-Box Optimizers through Symbolic Equation Learning, Twelfth International Conference on Learning Representations (ICLR), 2024 (31% of acceptance out of 7262 submissions)

Qidong Liu, Chaoyue Liu, Shaoyao Niu, Cheng Long, Jie Zhang and Mingliang Xu, 2D-Ptr: 2D Array Pointer Network for Solving the Heterogeneous Capacitated Vehicle Routing Problem, 23rd International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2024 (25% of acceptance, 229 out of 883 submissions)

Marcus de Carvalho, Mahardhika Pratama, Jie Zhang, Chua Haoyan and Edward Yapp, Towards Cross-Domain Continual Learning, 40th IEEE International Conference on Data Engineering (ICDE), 2024

Yuan Jiang, Zhiguang Cao, Yaoxin Wu, Wen Song and Jie Zhang, Ensemble-based Deep Reinforcement Learning for Vehicle Routing Problems under Distribution Shift, Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS), 2023 (26.1% of acceptance out of 12343 submissions)

Jianan Zhou, Yaoxin Wu, Wen Song, Zhiguang Cao and Jie Zhang, Towards Omni-generalizable Neural Methods for Vehicle Routing Problems, 40th International Conference on Machine Learning (ICML), 2023 (27.9% of acceptance out of 6538 submissions)

Wenhui Huang, Cong Zhang, Jingda Wu, Xiangkun He, Jie Zhang and Chen Lv, Sampling Efficient Deep Reinforcement Learning through Preference-Guided Stochastic Exploration, IEEE Transactions on Neural Networks and Learning Systems, 2023 (Impact Factor: 10.4)

Jingwen Li, Yining Ma, Zhiguang Cao, Yaoxin Wu, Wen Song, Jie Zhang and Yeow Meng Chee, Learning Feature Embedding Refiner for Solving Vehicle Routing Problems, IEEE Transactions on Neural Networks and Learning Systems, 2023 (Impact Factor: 10.4)

Shi Yuan Tang, Athirai A. Irissappane, Frans A. Oliehoek and Jie Zhang, Teacher-Apprentices RL (TARL): Leveraging Complex Policy Distribution through Generative Adversarial Hypernetwork in Reinforcement Learning, Autonomous Agents and Multi-Agent Systems, accepted, 2023 (Impact Factor: 2.475; conference version: Learning Complex Policy Distribution with CEM Guided Adversarial Hypernetwork, 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2021 (25% of acceptance, 152/612)

Kaixin Wang, Cheng Long, Darrell Joshua Ong, Jie Zhang and Xue-Ming Yuan, Single-site Perishable Inventory Management under Uncertainties: A Deep Reinforcement Learning Approach, IEEE Transactions on Knowledge and Data Engineering, 2023 (Impact Factor: 9.235)

Yaoxin Wu, Jianan Zhou, Zhiguang Cao, Wen Song, Jie Zhang and Zhenghua Chen, Learning Large Neighborhood Search for Vehicle Routing in Airport Ground Handling, IEEE Transactions on Knowledge and Data Engineering, 2023 (Impact Factor: 9.235)

Yaoxin Wu, Jianan Zhou, Yunwen Xia, Xianli Zhang, Zhiguang Cao and Jie Zhang, Neural airport ground handling, IEEE Transactions on Intelligent Transportation Systems (TITS), 2023 (Impact Factor: 9.551)

Yuan Jiang, Zhiguang Cao, Yaoxin Wu and Jie Zhang, Multi-View Graph Contrastive Learning for Solving Vehicle Routing Problems, 39th Conference on Uncertainty in Artificial Intelligence (UAI), 2023(31% of acceptance, 243/784)

Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang, Abhishek Gupta and Mingyan Simon Lin, Graph Learning Assisted Multi-Objective Integer Programming, Thirty-Sixth Conference on Neural Information Processing Systems (NeurIPS), 2022 (25% of acceptance out of 10411 submissions)

Yaoxin Wu, Wen Song, Zhiguang Cao and Jie Zhang, Learning Scenario Representation for Solving Two-stage Stochastic Integer Programs, 10th International Conference on Learning Representations (ICLR), 2022

Yuan Jiang, Yaoxin Wu, Zhiguang Cao and Jie Zhang, Learning to Solve Routing Problems via Distributionally Robust Optimization, Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI), 2022 (15% of acceptance, 1349/9251)

Rongkai Zhang, Cong Zhang, Zhiguang Cao, Wen Song, Bihan Wen, Jie Zhang, Puay Siew Tan and Justin Dauwels, Learning to Solve Multiple-TSP with Time Window and Rejection via Deep Reinforcement Learning, IEEE Transactions on Intelligent Transportation Systems (TITS), 2022 (Impact Factor: 9.551)

Yaoxin Wu, Wen Song, Zhiguang Cao and Jie Zhang, Learning Large Neighborhood Search Policy for Integer Programming , Thirty-fifth Annual Conference on Neural Information Processing Systems (NeurIPS), 2021 (spotlight presentation, 3% of acceptance out of 9122 submissions)

Liang Xin, Wen Song, Zhiguang Cao and Jie Zhang, NeuroLKH: Combining Deep Learning Model with Lin-Kernighan-Helsgaun Heuristic for Solving the Traveling Salesman Problem, Thirty-fifth Annual Conference on Neural Information Processing Systems (NeurIPS), 2021 (26% of acceptance out of 9122 submissions)

Yuan Jiang, Zhiguang Cao and Jie Zhang, Learning to Solve 3D Bin Packing Problem via Deep Reinforcement Learning and Constraint Programming, IEEE Transactions on Cybernetics, 2021(Impact Factor: 15.84)

Jingwen Li, Ruize Gao, Yining Ma, Zhiguang Cao, Andrew Lim, Wen Song and Jie Zhang, Deep Reinforcement Learning for Solving the Heterogeneous Capacitated Vehicle Routing Problem, IEEE Transactions on Cybernetics, 2021(Impact Factor: 15.84)

Hongliang Guo, Xuejie Hou, Zhiguang Cao and Jie Zhang, GP3: Gaussian Process Path Planning for Reliable Shortest Path in Transportation Networks, IEEE Transactions on Intelligent Transportation Systems, accepted, 2021 (Impact Factor: 8.41)

Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang and Andrew Lim, Learning Improvement Heuristics for Solving Routing Problems, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), accepted, 2021 (Impact Factor: 8.793)

Liang Xin, Wen Song, Zhiguang Cao and Jie Zhang, Multi-Decoder Attention Model with Embedding Glimpse for Solving Vehicle Routing Problems, 35th AAAI Conference on Artificial Intelligence (AAAI), 2021 (21% of acceptance, 1692/7911)

Kaixin Wang, Cheng Long, Yongxin Tong, Jie Zhang and Yi Xu, Adaptive Holding for Online Bottleneck Matching with Delays, SIAM International Conference on Data Mining (SDM), 2021 (21.25% of acceptance, 85/400)

Jingwen Li, Liang Xin, Zhiguang Cao, Andrew Lim, Wen Song and Jie Zhang, Heterogeneous Attentions for Solving Pickup and Delivery Problem via Deep Reinforcement Learning, IEEE Transactions on Intelligent Transportation Systems (ITS), accepted, 2021 (Impact Factor: 6.492)

Cong Zhang, Wen Song, Zhiguang Cao, Jie Zhang, Puay Siew Tan and Chi Xu, Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning, 34th Conference on Neural Information Processing Systems (NeurIPS), 2020 (20% of acceptance, 1900/9454)

Liang Xin, Wen Song, Zhiguang Cao and Jie Zhang, Step-wise Deep Learning Models for Solving Routing Problems, IEEE Transactions on Industrial Informatics, accepted, 2020 (Impact Factor: 9.112)

Monidipa Das, Mahardhika Pratama, Jie Zhang and Yew Soon Ong, A Skip-connected Evolving Recurrent Neural Network for Data Stream Classification under Label Latency Scenario, Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), 2020 (20.6% of acceptance, 1591/7737)

Chao Ma, Zhenbing Liu, Zhiguang Cao, Wen Song, Jie Zhang and Weiliang Zeng, Cost-sensitive Deep Forest for Price Prediction, Pattern Recognition, accepted, 2020 (Impact Factor: 5.898)

Yang Bao, Bin Ke, Bin Li, Y. Julia Yu, and Jie Zhang, Detecting Accounting Fraud in Publicly Traded U.S. Firms Using a Machine Learning Approach, Journal of Accounting Research, accepted, 2019 (Impact Factor: 4.891)

Monidipa Das, Mahardhika Pratama, Septiviana Savitri, and Jie Zhang, MUSE-RNN: A Multilayer Self-Evolving Recurrent Neural Network for Data Stream Classification, 19th IEEE International Conference on Data Mining (ICDM), 2019 (9.08% of acceptance, 95/1046)

Wen Song, Donghun Kang, Jie Zhang, Hui Xi and Zhiguang Cao, A Sampling Approach for Proactive Project Scheduling under Generalized Time-dependent Workability Uncertainty, Journal of Artificial Intelligence Research, accepted, 2019 (Impact Factor: 2.284)

Wen Song, Donghun Kang, Jie Zhang and Hui Xi, Risk-aware Proactive Scheduling via Conditional Value-at-Risk, 32nd AAAI Conference on Artificial Intelligence (AAAI), 2018 (24.6% of acceptance, 933/3800)

Zhiguang Cao, Hongliang Guo, Jie Zhang and Ulrich Fastenrath, A Multiagent-based Approach for Vehicle Routing by Considering both Arriving on Time and Total Travel Time, ACM Transactions on Intelligent Systems and Technology (TIST), accepted, 2017 (5-year Impact Factor: 9.15; conference version: Multiagent-based Route Guidance for Increasing the Chance of Arrival on Time, 30th AAAI Conference on Artificial Intelligence (AAAI), 2016 (26% of acceptance, 549/2132))

Wen Song, Donghun Kang, Jie Zhang and Hui Xi, Proactive Project Scheduling with Time-dependent Workability Uncertainty, 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2017 (26% of acceptance, 155/595)

Zhiguang Cao, Hongliang Guo, Jie Zhang, Frans Oliehoek and Ulrich Fastenrath, Maximizing the Probability of Arriving on Time: A Practical Q-Learning Method, 31st AAAI Conference on Artificial Intelligence (AAAI), 2017 (24.6% of acceptance, 638/2590)

Hongliang Guo, Zhiguang Cao, Jie Zhang, Dusit Niyato, Madhavan Seshadri and Ulrich Fastenrath, Routing Multiple Vehicles Cooperatively: Minimizing Road Network Breakdown Probability, IEEE Transactions on Emerging Topics in Computational Intelligence, accepted, 2017

Zhiguang Cao, Siwei Jiang, Jie Zhang and Hongliang Guo, A Unified Framework for Vehicle Rerouting and Traffic Light Control to Reduce Traffic Congestion, IEEE Transactions on Intelligent Transportation Systems (ITS), in press, 2016 (Impact Factor: 2.377; short conference version: A Pheromone-based Traffic Management Model for Vehicle Re-routing and Traffic Light Control, 13th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2014)

Hongliang Guo and Jie Zhang, A Distributed and Scalable Machine Learning Approach for Big Data, 25th International Joint Conference on Artificial Intelligence (IJCAI), 2016 (<25% of acceptance, 2294 submissions)

Siwei Jiang and Jie Zhang, A Simple and Fast Hypervolume Indicator-based Multiobjective Evolutionary Algorithm, IEEE Transactions on Cybernetics, 45(10), pp. 2202-2213, 2015 (Impact Factor: 3.236)

Zhiguang Cao, Hongliang Guo, Jie Zhang, Dusit Niyato, and Ulrich Fastenrath, Finding the Shortest Path in Stochastic Vehicle Routing: A Cardinality Minimization Approach, IEEE Transactions on Intelligent Transportation Systems (ITS), in press, 2015 (Impact Factor: 2.377)

Zhiguang Cao, Hongliang Guo, Jie Zhang, Dusit Niyato, and Ulrich Fastenrath, Improving the Efficiency of Stochastic Vehicle Routing: A Partial Lagrange Multiplier Method, IEEE Transactions on Vehicular Technology (TVT), in press, 2015 (Impact Factor: 1.978)

Siwei Jiang, Jie Zhang and Yew-Soon Ong, Multiobjective Optimization Based on Reputation, Information Sciences, Volume 286, pp. 125-146, 2014 (Impact Factor: 3.893; conference version: A Multiagent Evolutionary Framework based on Trust for Multiobjective Optimization, 11th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2012 (20.4% of acceptance, 137/671))

Siwei Jiang, Yew-Soon Ong and Jie Zhang, Consistencies and Contradictions of Performance Metrics in Multiobjective Optimization, IEEE Transactions on Cybernetics, 44(12), pp.2391-2404, 2014 (Impact Factor: 3.236)

Trust Modeling

Yuan Liu, Zehui Xiong, Qin Hu, Dusit Niyato, Jie Zhang, Chunyan Miao, Cyril Leung and Zhihong Tian, VRepChain: A Decentralized and Privacy-preserving Reputation System for Social Internet of Vehicles Based on Blockchain, IEEE Transactions on Vehicular Technology, accepted, 2022 (Impact Factor: 6.239)

Chuang Zhang, Yuan Liu, Xin Zhou, Zhihong Tian and Jie Zhang, A Semi-centralized Trust Management Model Based on Blockchain for Data Exchange in IoT System, IEEE Transactions on Services Computing, accepted, 2022 (Impact Factor: 8.216)

Hui Fang, Xiaoming Li and Jie Zhang, Integrating Social Influence Modeling and User Modeling for Trust Prediction in Signed Networks, Artificial Intelligence, accepted, 2021 (Impact Factor: 9.088)

Qiang He, Hui Fang and Jie Zhang, Dynamic Opinion Maximization in Social Networks, IEEE Transactions on Knowledge and Data Engineering, 2021 (Impact Factor: 4.935)

Leonit Zeynalvand, Tony T. Luo and Jie Zhang, COBRA: Context-aware Bernoulli Neural Networks for Reputation Assessment, Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), 2020 (20.6% of acceptance, 1591/7737)

Xiaoming Li, Hui Fang and Jie Zhang, Supervised User Ranking in Signed Social Networks, Thirty-Third AAAI Conference on Artificial Intelligence (AAAI), 2019 (16.2% of acceptance, 1150/7095)

Dongxia Wang, Tim Muller, Jie Zhang and Yang Liu, Information Theoretical Analysis of Unfair Rating Attacks under Subjectivity, IEEE Transactions on Information Forensics & Security (TIFS), accepted, 2019 (Impact Factor: 6.211)

Xiaoming Li, Hui Fang and Jie Zhang, FILE: A Novel Framework for Predicting Social Status in Signed Networks, 32nd AAAI Conference on Artificial Intelligence (AAAI), 2018 (24.6% of acceptance, 933/3800)

Shuo Chen, Athirai Irissappane and Jie Zhang, POMDP-based Decision Making for Fast Event Handling in VANETs, 32nd AAAI Conference on Artificial Intelligence (AAAI), 2018 (24.6% of acceptance, 933/3800)

Noel Sardana, Robin Cohen, Jie Zhang and Shuo Chen, A Bayesian multiagent trust model for social networks, IEEE Transactions on Computational Social Systems (TCSS), accepted, 2018

Chang Xu, Jie Zhang and Zhu Sun, Online Reputation Fraud Campaign Detection in User Ratings, 26th International Joint Conference on Artificial Intelligence (IJCAI), 2017 (26% of acceptance, 660/2540)

Qikun Xiang, Jie Zhang, Ido Nevat and Pengfei Zhang, A Trust-based Mixture of Gaussian Processes Model for Reliable Regression in Participatory Sensing, 26th International Joint Conference on Artificial Intelligence (IJCAI), 2017 (26% of acceptance, 660/2540; short version: A Trust-based Mixture of Gaussian Processes Model for Robust Participatory Sensing, 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS))

Chang Xu and Jie Zhang, Collusive Opinion Fraud Detection in Online Reviews: A Probabilistic Modeling Approach, ACM Transactions on the Web (TWEB), accepted, 2017 (Impact Factor: 1.595, conference version: Towards Collusive Fraud Detection in Online Reviews, IEEE International Conference on Data Mining series (ICDM), 2015 (18.2% of acceptance, 147/807))

Dongxia Wang, Tim Muller, Jie Zhang and Yang Liu, Is it Harmful when Advisors only Pretend to be Honest?, 30th AAAI Conference on Artificial Intelligence (AAAI), 2016 (26% of acceptance, 549/2132)

Athirai A. Irissappane, Frans A. Oliehoek and Jie Zhang, A Scalable Framework to Choose Sellers in E-Marketplaces Using POMDPs, 30th AAAI Conference on Artificial Intelligence (AAAI), 2016 (26% of acceptance, 549/2132)

Dongxia Wang, Tim Muller, Athirai A. Irissappane, Jie Zhang and Yang Liu, Using Information Theory to Improve the Robustness of Trust Systems, 14th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2015 (24.9% of acceptance, 167/670)

Athirai A. Irissappane, Jie Zhang, Frans Oliehoek and Partha S Dutta, Secure Routing in Wireless Sensor Networks via POMDPs, 25th International Joint Conference on Artificial Intelligence (IJCAI), 2015 (28.8% of acceptance, 575/1996)

Peng Zhou, Siwei Jiang, Athirai A. Irissappane, Jie Zhang, Jianying Zhou and Joseph Chee Ming Teo, Toward Energy-Efficient Trust System through Watchdog Optimization for WSNs, IEEE Transactions on Information Forensics & Security (TIFS), 10(3), pp. 613-625, 2015 (Impact Factor: 5.824)

Chang Xu and Jie Zhang, Combating Product Review Spam Campaigns via Multiple Heterogeneous Pairwise Features, 15th SIAM International Conference on Data Mining (SDM), 2015 (14.7% of acceptance)

Athirai A. Irissappane and Jie Zhang, A Case-Based Reasoning Framework to Choose Trust Models for Different E-Marketplace Environments, Journal of Artificial Intelligence Research, Volume 52, pp. 477-505, 2015 (5-yr Impact Factor: 1.685, conference version: A Framework to Choose Trust Models for Different E-Marketplace Environments, 23rd International Joint Conference on Artificial Intelligence (IJCAI), 2013 28% of acceptance, 413/1473)

Tim Muller, Yang Liu and Jie Zhang, The Fallacy of Endogenous Discounting of Trust Recommendations, 14th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2015 (24.9% of acceptance, 167/670)

Dongxia Wang, Tim Muller, Jie Zhang and Yang Liu, Quantifying Robustness of Trust Systems against Collusive Unfair Rating Attacks Using Information Theory, 25th International Joint Conference on Artificial Intelligence (IJCAI), 2015 (28.8% of acceptance, 575/1996)

Athirai A. Irissappane and Jie Zhang, Filtering Unfair Ratings from Dishonest Advisors in Multi-Criteria E-Markets: A Biclustering-Based Approach, Autonomous Agents and Multi-Agent Systems (JAAMAS), accepted, 2015 (short conference version, A Biclustering-Based Approach to Filter Dishonest Advisors in Multi-Criteria E-Marketplaces, 13th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2014)

Peng Zhou, Xiaojing Gu, Jie Zhang and Minrui Fei, A Priori Trust Inference with Context-Aware Stereotypical Deep Learning, Knowledge-Based Systems, 88(C), pp. 97-106, 2015 (Impact Factor: 2.947)

Hui Fang, Jie Zhang and Nadia Thalmann, Subjectivity Grouping: Learning from Users' Rating Behavior, 13th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2014 (23.8% of acceptance, 169/709)

Athirai A. Irissappane, Frans A. Oliehoek and Jie Zhang, A POMDP Based Approach to Optimally Select Sellers in Electronic Marketplace, 13th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2014 (23.8% of acceptance, 169/709)

Siyuan Liu, Jie Zhang, Chunyan Miao, Yin-Leng Theng and Alex C. Kot, An Integrated Clustering-Based Approach to Filtering Unfair Multi-Nominal Testimonies, Computational Intelligence, 30(2), pp. 316-341, 2014 (short conference version, iCLUB: An Integrated Clustering-Based Approach to Improve the Robustness of Reputation Systems, 10th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2011)

Siwei Jiang, Jie Zhang and Yew-Soon Ong, An Evolutionary Model for Constructing Robust Trust Networks, 12th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2013 (22.9% of acceptance, 140/612. Nomination of Best Student Paper Award)

Hui Fang, Jie Zhang and Nadia Thalmann, A Trust Model Stemmed from the Diffusion Theory for Opinion Evaluation, 12th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2013 (22.9% of acceptance, 140/612)

Hui Fang, Yang Bao and Jie Zhang, Misleading Opinions Provided by Advisors: Dishonesty or Subjectivity, 23rd International Joint Conference on Artificial Intelligence (IJCAI), 2013 (oral presentation, 13.2% of acceptance, 195/1473)

Chang Xu, Jie Zhang, Kuiyu Chang and Chong Long, Uncovering Collusive Spammers in Chinese Review Websites, 22nd ACM International Conference on Information and Knowledge Management (CIKM), 2013 (16.9% of acceptance, 143/848)

Qin Li, Amizah Malip, Keith Martin, Siaw-Lynn Ng and Jie Zhang, A Reputation-based Announcement Scheme for VANETs, IEEE Transactions on Vehicular Technology (TVT), 61(9), pp. 4095-4108, 2012 (Impact Factor: 1.921)

Carol J Fung, Jie Zhang and Raouf Boutaba, Effective Acquaintance Management based on Bayesian Learning for Distributed Intrusion Detection Networks, IEEE Transactions on Network and Service Management (TNSM), 9(3), pp. 320-332, 2012 (Impact Factor: 3.134; conference version: Effective Acquaintance Management for Collaborative Intrusion Detection Networks, 6th International Conference on Network and Service Management (CNSM), 2010 15% of acceptance, Best Student Paper Award)

Carol J Fung, Jie Zhang, Issam Aib and Raouf Boutaba, Dirichlet-based Trust Management for Effective Collaborative Intrusion Detection Networks, IEEE Transactions on Network and Service Management (TNSM), 8(2), pp. 79-91, 2011 (Impact Factor: 3.134; conference version: Robust and Scalable Trust Management for Collaborative Intrusion Detection, 11th IFIP/IEEE International Symposium on Integrated Network Management (IM), 2009 Best Paper Award)

Umar F. Minhas, Jie Zhang, Thomas Tran and Robin Cohen, A Multi-faceted Approach to Modeling Agent Trust for Effective Communication in the Application of Mobile Ad Hoc Vehicular Networks, IEEE Transactions on Systems, Man, and Cybernetics--Part C: Applications and Reviews (SMCC), 41(3), pp. 407-420, 2011 (Impact Factor: 2.105)

Jie Zhang and Robin Cohen, Evaluating the Trustworthiness of Advice about Selling Agents in E-Marketplaces: A Personalized Approach, Electronic Commerce Research and Applications (ECRA), 7(3), pp. 330-340, 2008 (Impact Factor: 1.946, one of 6/112 papers recommended from International Conference on Electronic Commerce ICEC'06: Trusting Advice from Other Buyers in E-Marketplaces: The Problem of Unfair Ratings)

Incentive and Trust, Mechanism Design, Multi-Agent System

Jing Sun, Shuo Chen, Cong Zhang, Yining Ma and Jie Zhang, Decision-making with Speculative Opponent Models, IEEE Transactions on Neural Networks and Learning Systems, 2024 (Impact Factor: 10.451)

Liwei Huang, Mingsheng FU, Ananya Rao, Athirai Irissappane, Jie, Zhang and Cheng-Zhong Xu, A Distributional Perspective on Multi-agent Cooperation with Deep Reinforcement Learning, IEEE Transactions on Neural Networks and Learning Systems, accepted, 2022 (Impact Factor: 10.451)

Shuo Chen, Ewa Andrejczuk, Zhiguang Cao and Jie Zhang, AATEAM: Achieving the Ad Hoc Teamwork by Employing the Attention Mechanism, Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), 2020 (20.6% of acceptance, 1591/7737)

Shuo Chen, Athirai A. Irissappane, Ewa Andrejczuk and Jie Zhang, ATSIS: Achieving the Ad hoc Teamwork by Sub-task Inference and Selection, 29th International Joint Conference on Artificial Intelligence (IJCAI), 2019 (17.9% of acceptance, 850/4752)

Zehong Hu, Zhen Wang, Zhao Li, Shichang Hu, Shasha Ruan and Jie Zhang, Fraud Regulating Policy for E-Commerce via Constrained Contextual Bandits, 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2019 (24.2% of acceptance, 189/781)

Tony Luo, Jianwei Huang, Salil Kanhere, Jie Zhang and Sajal Das, Improving IoT Data Quality in Mobile Crowdsensing: A Cross Validation Approach, IEEE Internet of Things Journal, accepted, 2019(Impact Factor: 5.863)

Zehong Hu and Jie Zhang, General Robustness Evaluation of Incentive Mechanism Against Bounded Rationality Using Continuum-Armed Bandits, Thirty-Third AAAI Conference on Artificial Intelligence (AAAI), 2019 (16.2% of acceptance, 1150/7095)

Zehong Hu, Yitao Liang, Jie Zhang and Yang Liu, Inference Aided Reinforcement Learning for Incentive Mechanism Design in Crowdsourcing, 32nd Conference on Neural Information Processing Systems (NeurIPS), 2018 (20.8% of acceptance, 1011/4856)

Zehong Hu and Jie Zhang, A Novel Strategy for Active Task Assignment in Crowd Labeling, 27th International Joint Conference on Artificial Intelligence (IJCAI), 2018 (20% of acceptance, 710/3470)

Zehong Hu and Jie Zhang, Towards General Robustness Evaluation of Incentive Mechanism Against Bounded Rationality, IEEE Transactions on Computational Social Systems (TCSS), accepted, 2018

Zehong Hu and Jie Zhang, Optimal Posted-Price Mechanism in Microtask Crowdsourcing, 26th International Joint Conference on Artificial Intelligence (IJCAI), 2017 (26% of acceptance, 660/2540)

Wen Song, Donghun Kang, Jie Zhang and Hui Xi, A Multi-Unit Combinatorial Auction based Approach for Decentralized Multi-Project Scheduling, Autonomous Agents and Multi-Agent Systems (JAAMAS), 2017 (conference version: Decentralized Multi-Project Scheduling via Multi-Unit Combinatorial Auction, 15th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2016 (24.9% of acceptance, 137/550))

Zehong Hu, Meng Sha, Moath Jarrah, Jie Zhang and Hui Xi, Efficient Computation of Emergent Equilibrium in Agent-Based Simulation, 30th AAAI Conference on Artificial Intelligence (AAAI), 2016 (26% of acceptance, 549/2132)

Yuan Liu, Jie Zhang, Bo An and Sandip Sen, A Simulation Framework for Measuring Robustness of Incentive Mechanisms and Its Implementation in Reputation Systems, Autonomous Agents and Multi-Agent Systems (JAAMAS), 30(4), pp. 581-600, 2016 (early short versions: A Practical Robustness Measure of Incentive Mechanisms, 13th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2014; Robustness Evaluation of Incentive Mechanisms, 12th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2013)

Akin Gunay, Yang Liu and Jie Zhang, PROMOCA: Probabilistic Modeling and Analysis of Agent Behaviors in Commitment Protocols, Journal of Artificial Intelligence Research, accepted, 2016 (5-yr Impact Factor: 2.32, conference version: Automated Analysis of Commitment Protocols using Probabilistic Model Checking, Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI), 2015 26.67% of acceptance, 531/1991)

Songzheng Song, Jianye Hao, Yang Liu, Jun Sun, Ho-fung Leung and Jie Zhang, Improved EGT-based Robustness Analysis of Negotiation Strategies in Multi-agent Systems via Model Checking, IEEE Transactions on Human-Machine Systems (THMS), 46(2), pp. 197-208, 2016 (5-yr Impact Factor: 2.428)

Zeinab Noorian, Jie Zhang, Yuan Liu, Stephen Marsh, and Michael Fleming, Trust-Oriented Buyer Strategies for Seller Reporting and Selection in Competitive Electronic Marketplaces, Autonomous Agents and Multi-Agent Systems (JAAMAS), 28(6), pp. 896-933, 2014

Jie Zhang and Robin Cohen, A Framework for Trust Modeling in Multiagent Electronic Marketplaces with Buying Advisors to Consider Varying Seller Behavior and the Limiting of Seller Bids, ACM Transactions on Intelligent Systems and Technology (TIST), 4(2), 2013 (Impact Factor: 9.39)

Jie Zhang and Robin Cohen, Design of a Mechanism for Promoting Honesty in E-Marketplaces, 22nd Conference on Artificial Intelligence (AAAI), 2007 (27% of acceptance, 253/921, top 5% (47/921) paper slated for additional poster presentation, short version: An Incentive Mechanism for Eliciting Fair Ratings of Sellers in E-Marketplaces, 6th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2007)

Le-Hung Vu, Jie Zhang and Karl Aberer, Using Identity Premium for Cooperation Enforcement and Whitewashing Prevention, Computational Intelligence, 30(4), pp. 771-797, 2014

Jie Zhang, Robin Cohen and Kate Larson, Combining Trust Modeling and Mechanism Design for Promoting Honesty in E-Marketplaces, Computational Intelligence, 28(4), pp. 549-578, 2012 (conference version: Leveraging a Social Network of Trust for Promoting Honesty in E-Marketplaces, IFIP WG 11.11 International Conference on Trust Management IFIPTM'10)

PhD Thesis

Promoting Honesty in E-Marketplaces: Combining Trust Modeling and Incentive Mechanism Design
University of Waterloo, Canada, 2009
Supervisor: Professor Robin Cohen
External Committee Member: Professor Munindar P. Singh

Full List of Publications

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