E-commerce platforms explore the interaction between users and digital content -user generated streams of events -to build and maintain dynamic user preference models which are used to make meaningful recommendations. However, the accuracy of these incremental models is critically affected by the choice of hyper-parameters. So far, the incremental recommendation algorithms used to process data streams rely on human expertise for hyper-parameter tuning. In this paper we apply our Self

more »

... eter Tuning (SPT) algorithm to incremental recommendation algorithms. SPT uses the Nelder & Mead optimisation algorithm to perform hyper-parameter tuning. It creates three models with different hyper-parameters, assesses them at dynamic size intervals and applies the Nelder & Mead operators to update their hyperparameters until they converge. The main contribution of this work is the adaptation of the SPT method to incremental matrix factorisation recommendation algorithms. The proposed method was evaluated with well-known recommendation data sets. The results show that SPT systematically improves data stream recommendations.