Mining User Mobility Features for Next Place Prediction in Location-Based Services

By Anastasios Noulas, Salvatore Scellato, Neal Kiritkumar Lathia, Cecilia Mascolo. 2012 IEEE 12th International Conference on Data Mining, 2012

360 citations recorded by the source corpus.

Subfields: Automotive Engineering, Marketing, Transportation

View Paper: https://www.semanticscholar.org/paper/2bf11b00938e73468e3ab02dfe678985212f1aea

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

1. Chau Yuen
2. G. Wang

In Marketing

1. Miriam J. Metzger
2. Brent J. Hecht

In Transportation

1. Philippe Cudré-Mauroux
2. Dingqi Yang
3. Bingqing Qu
4. Isaac L. Johnson
5. Johannes Schöning
6. Chau Yuen
7. Billy Pik Lik Lau
8. Ben Y. Zhao

Citations in the corpus

• The Geography and Importance of Localness in Geotagged Social Media (2016, methodology): “Swarm check-ins shared via Twitter have been used extensively in the past (e.g. [13,35]).”
• Understanding Urban Human Mobility through Crowdsensed Data (2018, background): “S.3 - Individual Activity-Based Mobility Patterns: This subject is about gaining insights of people’s activities (visits to places), such as identifying activity categories (e.g. home, working, shopping) [7] and predicting next visiting places [8].”
• Revisiting User Mobility and Social Relationships in LBSNs: A Hypergraph Embedding Approach (2019, background): “[21] investigated the next place prediction problem and found that besides a user’s own check-in history, a “social filtering” feature”
• On the validity of geosocial mobility traces (2013, methodology): “Between January 2013 and July 2013, we received data from 244 users worldwide, where each user’s measurement data covered an average of 14 days.”