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TribeFlow: Mining & Predicting User Trajectories

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arxiv 1511.01032 v2 pith:LTRV6ICC submitted 2015-11-03 cs.SI physics.data-anphysics.soc-phstat.ML

TribeFlow: Mining & Predicting User Trajectories

classification cs.SI physics.data-anphysics.soc-phstat.ML
keywords nexttribeflowuserwillpredictiontrajectorieschallengesdoing
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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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