His primary areas of investigation include Information retrieval, Data mining, World Wide Web, Social network and Point of interest. Gao Cong regularly ties together related areas like The Internet in his Information retrieval studies. The study incorporates disciplines such as Machine learning, Row and Search engine indexing, Artificial intelligence in addition to Data mining.
His Web content study, which is part of a larger body of work in World Wide Web, is frequently linked to Baseline and Focus, bridging the gap between disciplines. Gao Cong has researched Social network in several fields, including Graphical model and Profiling. His Point of interest research is multidisciplinary, incorporating elements of Breadth-first search, Recommender system, Graph and Service.
His primary scientific interests are in Data mining, Information retrieval, Artificial intelligence, Theoretical computer science and Machine learning. His work deals with themes such as Tree, Scalability, Row and Cluster analysis, which intersect with Data mining. The Information retrieval study which covers World Wide Web that intersects with Point of interest.
His work carried out in the field of Artificial intelligence brings together such families of science as Natural language processing and Pattern recognition. In his work, Social network is strongly intertwined with Graph, which is a subfield of Theoretical computer science. His work is dedicated to discovering how Web search query, Query language are connected with Query expansion and other disciplines.
Artificial intelligence, Theoretical computer science, Trajectory, Graph and Machine learning are his primary areas of study. The Artificial intelligence study combines topics in areas such as Sequence modeling and Pattern recognition. In his study, Human–computer interaction is strongly linked to Recommender system, which falls under the umbrella field of Theoretical computer science.
His work in Graph covers topics such as Graph which are related to areas like Social network, Session and Pairwise comparison. Gao Cong performs integrative Throughput and Data mining research in his work. His Data mining study combines topics in areas such as Stability and Dimension.
Gao Cong spends much of his time researching Recommender system, Theoretical computer science, Feature learning, Artificial intelligence and Collaborative filtering. His Recommender system research integrates issues from Session, Graph, Pairwise comparison and Graph. His research integrates issues of Artificial neural network, Generative model and Statistical model in his study of Feature learning.
Gao Cong combines subjects such as Machine learning and Pattern recognition with his study of Artificial intelligence. His study in Collaborative filtering is interdisciplinary in nature, drawing from both Boosting and Human–computer interaction. His Metric research focuses on Ranking and how it connects with Recurrent neural network.
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.
Time-aware point-of-interest recommendation
Quan Yuan;Gao Cong;Zongyang Ma;Aixin Sun.
international acm sigir conference on research and development in information retrieval (2013)
Efficient retrieval of the top-k most relevant spatial web objects
Gao Cong;Christian S. Jensen;Dingming Wu.
very large data bases (2009)
Community-based greedy algorithm for mining top-K influential nodes in mobile social networks
Yu Wang;Gao Cong;Guojie Song;Kunqing Xie.
knowledge discovery and data mining (2010)
Improving data quality: consistency and accuracy
Gao Cong;Wenfei Fan;Floris Geerts;Xibei Jia.
very large data bases (2007)
Mining significant semantic locations from GPS data
Xin Cao;Gao Cong;Christian S. Jensen.
very large data bases (2010)
Collective spatial keyword querying
Xin Cao;Gao Cong;Christian S. Jensen;Beng Chin Ooi.
international conference on management of data (2011)
Personalized ranking metric embedding for next new POI recommendation
Shanshan Feng;Xutao Li;Yifeng Zeng;Gao Cong.
international conference on artificial intelligence (2015)
Spatial keyword query processing: an experimental evaluation
Lisi Chen;Gao Cong;Christian S. Jensen;Dingming Wu.
very large data bases (2013)
Rank-GeoFM: A Ranking based Geographical Factorization Method for Point of Interest Recommendation
Xutao Li;Gao Cong;Xiao-Li Li;Tuan-Anh Nguyen Pham.
international acm sigir conference on research and development in information retrieval (2015)
Finding question-answer pairs from online forums
Gao Cong;Long Wang;Chin-Yew Lin;Young-In Song.
international acm sigir conference on research and development in information retrieval (2008)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: