Mining user similarity based on location history

By Quannan Li, Yu Zheng, Xing Xie, Yukun Chen, Wei-Ying Ma. ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems, 2008

748 citations recorded by the source corpus.

Subfields: Geography, Planning and Development, Transportation, Signal Processing

View Paper: https://www.semanticscholar.org/paper/c801dfe2951103ce3c303274bdb459d3240edcbb

How researchers see this paper

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

In Geography, Planning and Development

1. Yu Zheng
2. Xing Xie
3. Mohamed F. Mokbel
4. Jie Bao
5. Vincent Wenchen Zheng
6. Qiang Yang
7. Wei-Ying Ma
8. Jing Yuan

In Transportation

1. Yu Zheng
2. Xing Xie
3. Jie Bao
4. Mohamed F. Mokbel
5. Wei-Ying Ma
6. David Wilkie
7. Qiang Yang
8. Jing Yuan

In Signal Processing

1. Yu Zheng
2. Xing Xie
3. Jie Bao
4. Mohamed F. Mokbel
5. Qiang Yang
6. David Wilkie
7. Wei-Ying Ma
8. Jing Yuan

Citations in the corpus

• Recommendations in location-based social networks: a survey (2015, background): “2011 [134] further extends the user similarity measure framework presented in [56] by considering the sequences of locations at different spatial granularities.”
• GeoLife: A Collaborative Social Networking Service among User, Location and Trajectory (2010, methodology): “In this paper, we introduce our project GeoLife [1, 2, 5 14], which is a social networking service incorporating users, locations and user-generated GPS trajectories.”
• Unveiling the complexity of human mobility by querying and mining massive trajectory data (2011, methodology): “The construction of the social network is based on efficient retrieval of similar trajectories [10], on spatiotemporal data mining algorithms [46] and a recommender system [35].”
• Identifying Important Places in People's Lives from Cellular Network Data (2011, positive): “Previous work [1, 16–18] has also explored how semantically meaningful places can be discovered based on series of GPS coordinates.”
• Mining Individual Life Pattern Based on Location History (2009, methodology): “[18] conducts similar work, while based on GSM network.”
• Understanding the Lifestyle of Older Population: Mobile Crowdsensing Approach (2019, methodology): “1) Validation-Based Stay Points Detection: VSPD algorithm is an extension of the stay point detection algorithm [24], in which an additional function is added to cope with indoor GPS accuracy issue.”
• Trajectory Data Mining (2015, methodology): “[54] first proposed the stay point detection algorithm.”
• Mining interesting locations and travel sequences from GPS trajectories (2009, methodology): “Figure 18 A) demonstrates the correlations between users in the GeoLife community.”
• Urban computing with taxicabs (2011, background): “, estimating the similarity between users in terms of their location histories [2][3][9], extracting social structures from mobile phone data [4], enabling friend and location recommenders in the real world [17][18], and studying the influence of pervasive systems on people in urban spaces [8].”
• Learning travel recommendations from user-generated GPS traces (2011, neutral): “…real-world location histories, some recommender systems, such as Geowhiz [Horozov et al. 2006], CityVoyager [Takeuchi and Sugimoto 2006], and GeoLife [Zheng et al. 2009a; Li et al. 2008; Zheng et al. 2010b], have been designed to recommend geographic locations like shops or restaurants to users.”
• Beyond "local", "categories" and "friends": clustering foursquare users with latent "topics" (2012, methodology): “One obvious use of the clusters discovered would be to design a recommendation system, as is done in [27] with the user similarity metric developed first in [10].”
• Bridging the gap between physical location and online social networks (2010, background): “To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.”
• SensLoc: sensing everyday places and paths using less energy (2010, methodology): “designed a time and distance based clustering algorithm to find significant locations and is used widely by others using GPS trajectories to find significant locations [18, 26].”
• Human mobility: Models and applications (2017, background): “Since its release, GeoLife has been used in studies of both human mobility [59, 60] and social interactions [61].”
• Collaborative location and activity recommendations with GPS history data (2010, methodology): “Finally, we apply a collective matrix factorization method to mine interesting locations and activities, and use them to recommend to the users where they can visit if they want to perform some specific activities and what they can do if they visit some specific places.”