What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Database
Xiaofang Zhou mainly investigates Data mining, Artificial intelligence, Machine learning, Cluster analysis and Pattern recognition.
His Data mining research focuses on Query optimization in particular.
Xiaofang Zhou works mostly in the field of Artificial intelligence, limiting it down to topics relating to Computer vision and, in certain cases, Representation.
His research investigates the connection between Machine learning and topics such as Location-based service that intersect with problems in Mobile computing.
Xiaofang Zhou focuses mostly in the field of Cluster analysis, narrowing it down to matters related to Variety and, in some cases, Fleet management, Trajectory pattern and Focus.
His work in the fields of Pattern recognition, such as Feature selection and Discriminative model, overlaps with other areas such as Sparse matrix and Spatiotemporal pattern.
His most cited work include:
- l 2,1 -norm regularized discriminative feature selection for unsupervised learning (468 citations)
- Advances in Spatial and Temporal Databases (424 citations)
- Computing with Spatial Trajectories (422 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Data mining, Artificial intelligence, Information retrieval, World Wide Web and Theoretical computer science.
His studies examine the connections between Data mining and genetics, as well as such issues in Spatial database, with regards to Spatial query.
His Artificial intelligence research includes elements of Natural language processing, Machine learning, Computer vision and Pattern recognition.
Xiaofang Zhou interconnects Ranking, Web page and Database in the investigation of issues within Information retrieval.
Many of his studies on Theoretical computer science involve topics that are commonly interrelated, such as Graph.
His Query optimization research is multidisciplinary, incorporating elements of Query expansion, Web query classification and Sargable.
He most often published in these fields:
- Data mining (32.95%)
- Artificial intelligence (19.97%)
- Information retrieval (18.64%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (19.97%)
- Machine learning (8.15%)
- Data mining (32.95%)
In recent papers he was focusing on the following fields of study:
Xiaofang Zhou spends much of his time researching Artificial intelligence, Machine learning, Data mining, Cluster analysis and Theoretical computer science.
His work carried out in the field of Artificial intelligence brings together such families of science as Graph and Natural language processing.
His Feature learning study in the realm of Machine learning connects with subjects such as Online and offline.
In his works, Xiaofang Zhou undertakes multidisciplinary study on Data mining and Road networks.
His biological study spans a wide range of topics, including Data stream, Robust statistics and Algorithm.
His Theoretical computer science study combines topics in areas such as Scheduling and Graph.
Between 2018 and 2021, his most popular works were:
- Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation (59 citations)
- Graph contextualized self-attention network for session-based recommendation (48 citations)
- Social Influence-Based Group Representation Learning for Group Recommendation (46 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Database
Xiaofang Zhou focuses on Artificial intelligence, Data mining, Machine learning, Theoretical computer science and Recurrent neural network.
The Artificial intelligence study combines topics in areas such as Natural language processing and Pattern recognition.
While working in this field, Xiaofang Zhou studies both Data mining and Measure.
His Machine learning research is multidisciplinary, incorporating elements of Training set, Relationship extraction, Crowdsourcing, Sentence and Graph embedding.
He works mostly in the field of Theoretical computer science, limiting it down to concerns involving Graph and, occasionally, Hop, Scheduling and Speedup.
His work is dedicated to discovering how Cluster analysis, Unsupervised learning are connected with Robustness and other disciplines.
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