Representation learning to a High-Dimensional and Incomplete (HDI) matrix can be performed efficiently by an Alternating-direction-method-based Nonnegative Latent Factor (ANLF) model. However, it introduces multiple hyper-parameters into the learning process, which should be chosen with care to enable its superior performance. Its hyper-parameter adaptation is desired for enhancing its scalability. Targeting at this issue, this paper proposes an Adaptive Alternating-direction-method-based Nonnegative Latent Factor (A2NLF) model, whose hyper-parameter adaptation is implemented following the principle of particle swarm optimization. Empirical studies on nonnegative HDI matrices generated by real applications indicate that A2NLF outperforms several state-of-the-art models in terms of computational efficiency, as well as maintains highly competitive estimation accuracy for an HDI matrix’s missing data.