In this brief, a class of weighted quantized kernel recursive least squares (WQKRLS) algorithms is proposed to efficiently improve the performance of online applications. In the proposed WQKRLS, an online vector quantization with weighted outputs is incorporated into quantized kernel recursive least squares. The resulting desired outputs are smoothed by exponential weights. In addition, the members of the dictionary are updated by the steepest descent method for further performance improvement. Simulations illustrate the superior performance of the proposed WQKRLS.