Mixed logit models with unobserved inter-and intra-individual heterogeneity hierarchically extend standard mixed logit models by allowing tastes to vary randomly both across individuals and across choice situations encountered by the same individual. Recent work advocates using these models in choice-based recommender systems under the premise that mixed logit models with unobserved inter-and intra-individual heterogeneity afford personalised preference estimation and prediction. In this study, we evaluate the ability of mixed logit with unobserved inter-and intra-individual heterogeneity to produce accurate individual-level predictions of choice behaviour. Using simulated and real data, we show that mixed logit models with unobserved inter-and intra-individual heterogeneity do not provide significant improvements in choice prediction accuracy over standard mixed logit models, which only account for inter individual taste variation. We make these observations even in scenarios with high levels of intra-individual taste variation and when the number of choice situations per decision maker is large. Also, the estimation of mixed logit with unobserved inter-and intra-individual heterogeneity requires at least seven times as much computation time as the estimation of standard mixed logit. Drawing from recent advances in machine learning and econometrics, we discuss alternative modelling approaches that can capture richer dependencies between decision-makers, alternatives and attributes.