TribeFlow: Mining & Predicting User Trajectories
November 03, 2015 ยท Declared Dead ยท ๐ The Web Conference
"No code URL or promise found in abstract"
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Authors
Flavio Figueiredo, Bruno Ribeiro, Jussara Almeida, Christos Faloutsos
arXiv ID
1511.01032
Category
cs.SI: Social & Info Networks
Cross-listed
physics.data-an,
physics.soc-ph,
stat.ML
Citations
51
Venue
The Web Conference
Last Checked
3 months ago
Abstract
Which song will Smith listen to next? Which restaurant will Alice go to tomorrow? Which product will John click next? These applications have in common the prediction of user trajectories that are in a constant state of flux over a hidden network (e.g. website links, geographic location). What users are doing now may be unrelated to what they will be doing in an hour from now. Mindful of these challenges we propose TribeFlow, a method designed to cope with the complex challenges of learning personalized predictive models of non-stationary, transient, and time-heterogeneous user trajectories. TribeFlow is a general method that can perform next product recommendation, next song recommendation, next location prediction, and general arbitrary-length user trajectory prediction without domain-specific knowledge. TribeFlow is more accurate and up to 413x faster than top competitors.
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