Intelligent career planning via stochastic subsampling reinforcement learning.

ABSTRACT: Career planning consists of a series of decisions that will significantly impact one's life. However, current recommendation systems have serious limitations, including the lack of effective artificial intelligence algorithms for long-term career planning, and the lack of efficient reinforcement learning (RL) methods for dynamic systems. To improve the long-term recommendation, this work proposes an intelligent sequential career planning system featuring a career path rating mechanism and a new RL method coined as the stochastic subsampling reinforcement learning (SSRL) framework. After proving the effectiveness of this new recommendation system theoretically, we evaluate it computationally by gauging it against several benchmarks under different scenarios representing different user prefe

SUBMITTER: Guo P 

PROVIDER: S-EPMC9117248 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature