  In sequential decision-making scenarios i.e., mobile health recommendation
systems revenue management contextual multi-armed bandit algorithms have
garnered attention for their performance. But most of the existing algorithms
are built on the assumption of a strictly parametric reward model mostly linear
in nature. In this work we propose a new algorithm with a semi-parametric
reward model with state-of-the-art complexity of upper bound on regret amongst
existing semi-parametric algorithms. Our work expands the scope of another
representative algorithm of state-of-the-art complexity with a similar reward
model by proposing an algorithm built upon the same action filtering procedures
but provides explicit action selection distribution for scenarios involving
more than two arms at a particular time step while requiring fewer
computations. We derive the said complexity of the upper bound on regret and
present simulation results that affirm our methods superiority out of all
prevalent semi-parametric bandit algorithms for cases involving over two arms.
