Jie Zhang spends much of his time researching Computer security, Recommender system, Data mining, Robustness and Information retrieval. His work deals with themes such as Scalability, Buying agent, Multi-agent system, Vehicular ad hoc network and Computer network, which intersect with Computer security. His Recommender system study integrates concerns from other disciplines, such as Bayesian probability, Artificial intelligence and Competence.
His research in Data mining intersects with topics in Similarity, Server log and Web service. His research in Robustness tackles topics such as Intrusion detection system which are related to areas like Peer-to-peer and Correctness. His research investigates the link between Information retrieval and topics such as Order that cross with problems in Algorithm design, Advice and Preference.
Jie Zhang focuses on Artificial intelligence, Computer security, Machine learning, Recommender system and Robustness. His Artificial intelligence study typically links adjacent topics like Data mining. His study in Information retrieval extends to Data mining with its themes.
His work is connected to Collaborative filtering and Cold start, as a part of Recommender system. Robustness is closely attributed to Incentive in his work. Jie Zhang is interested in Social network, which is a field of World Wide Web.
Jie Zhang mainly focuses on Artificial intelligence, Reinforcement learning, Theoretical computer science, Recommender system and Machine learning. His Node research extends to Artificial intelligence, which is thematically connected. His Reinforcement learning study combines topics in areas such as Routing, Constraint, Heuristics, Scheme and Job shop scheduling.
His research on Recommender system also deals with topics like
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Heuristics, Recommender system and Reinforcement learning. His biological study spans a wide range of topics, including Field and Random walk. The Machine learning study combines topics in areas such as Data stream, Layer and Accounting.
His work in Heuristics tackles topics such as Travelling salesman problem which are related to areas like Vehicle routing problem and Selection. His Recommender system study is concerned with the larger field of World Wide Web. His Reinforcement learning research incorporates elements of Scheme, Path, Graph and Inference.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Improved Light Harvesting and Improved Efficiency by Insertion of an Optical Spacer (ZnO) in Solution-Processed Small-Molecule Solar Cells
Aung Ko Ko Kyaw;Dong Hwan Wang;David Wynands;Jie Zhang.
Nano Letters (2013)
Efficient Solution‐Processed Small‐Molecule Solar Cells with Inverted Structure
Aung Ko Ko Kyaw;Dong Hwan Wang;Vinay Gupta;Vinay Gupta;Jie Zhang.
Advanced Materials (2013)
TrustSVD: collaborative filtering with both the explicit and implicit influence of user trust and of item ratings
Guibing Guo;Jie Zhang;Neil Yorke-Smith.
national conference on artificial intelligence (2015)
Collaborative Security: A Survey and Taxonomy
Guozhu Meng;Yang Liu;Jie Zhang;Alexander Pokluda.
ACM Computing Surveys (2015)
N-annulated perylene as an efficient electron donor for porphyrin-based dyes: enhanced light-harvesting ability and high-efficiency Co(II/III)-based dye-sensitized solar cells.
Jie Luo;Mingfei Xu;Renzhi Li;Kuo-Wei Huang.
Journal of the American Chemical Society (2014)
A Survey on Trust Management for VANETs
advanced information networking and applications (2011)
Consistencies and contradictions of performance metrics in multiobjective optimization.
Siwei Jiang;Yew-Soon Ong;Jie Zhang;Liang Feng.
IEEE Transactions on Systems, Man, and Cybernetics (2014)
TopicMF: simultaneously exploiting ratings and reviews for recommendation
Yang Bao;Hui Fang;Jie Zhang.
national conference on artificial intelligence (2014)
Merging trust in collaborative filtering to alleviate data sparsity and cold start
Guibing Guo;Jie Zhang;Daniel Thalmann.
Knowledge Based Systems (2014)
A novel Bayesian similarity measure for recommender systems
Guibing Guo;Jie Zhang;Neil Yorke-Smith.
international joint conference on artificial intelligence (2013)
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