The query-flow graph: model and applications
By Paolo Boldi, Francesco Bonchi, Carlos Castillo, Debora Donato, Aristides Gionis, Sebastiano Vigna. International Conference on Information and Knowledge Management, 2008
358 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Information Systems, Signal Processing
View Paper: https://www.semanticscholar.org/paper/5105b69b51180a8f5067fad26780178913c7d740
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
- Peter Bhlmann
- M. J. van der Laan
- Michael J. Kane
- Tao Mei
- Aixin Sun
- Petros Drineas
- Chenliang Li
- David P. Woodruff
In Information Systems
- Peter Bhlmann
- Tao Mei
- M. J. van der Laan
- Yong Rui
- Winston H. Hsu
- Aixin Sun
- Petros Drineas
- Michael J. Kane
In Signal Processing
- Peter Bhlmann
- M. J. van der Laan
- Michael J. Kane
- Tao Mei
- Yong Rui
- Winston H. Hsu
- Aixin Sun
- Petros Drineas
Citations in the corpus
- Handbook of Big Data (2016, background): “Alternatively, large graph mining produces similar information for recommendation, suggestion, and prediction from messy data [9,10]; it can also tell us how to optimize a data infrastructure to improve response time [11]; and it can tell us when and how our data are anomalous [12].”
- A Survey on Deep Learning for Named Entity Recognition (2018, background): “There are also studies utilizing named entities for an enhanced user experience, such as query recommendation [34], query autocompletion [35], [36] and entity cards [37], [38].”
- Learning to personalize trending image search suggestion (2014, background): “Two consecutive queries issued within a specific time threshold by a single user are considered in the same search session as defined by [5].”
- Semantic Matching in Search (2014, neutral): “,[27]).”