Markovletics: Methods and A Novel Application for Learning Continuous-Time Markov Chain Mixtures

arXiv:2402.17730v1 Announce Type: new
Abstract: Sequential data naturally arises from user engagement on digital platforms like social media, music streaming services, and web navigation, encapsulating evolving user preferences and behaviors through continuous information streams. A notable unresolved query in stochastic processes is learning mixtures of continuous-time Markov chains (CTMCs). While there is progress in learning mixtures of discrete-time Markov chains with recovery guarantees [GKV16,ST23,KTT2023], the continuous scenario uncovers unique unexplored challenges. The intrigue in CTMC mixtures stems from their potential …

abstract application arxiv continuous cs.lg data digital engagement information markov media music music streaming music streaming services navigation novel platforms processes query services social social media stochastic streaming streaming services through type user engagement web web navigation