Self hyper-parameter tuning for stream recommendation algorithms

Abstract

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 mean-ingful 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 work we apply our Self Hyper-Parameter Tuning (SPT) algorithm to incremental recommendation algorithms. SPT adapts the Melder-Mead optimi-sation algorithm to perform hyper-parameter tuning. First, it creates three models with random hyper-parameter values and, then, at dynamic size intervals, assesses and applies the Melder-Mead operators to update their hyper-parameters until the models converge. The main contribu-tion 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.