Recovering individual emotional states from sparse ratings using collaborative filtering

A fundamental challenge in emotion research is measuring feeling states with high granularity and temporal precision without disrupting the emotion generation process. Here we introduce and validate a new approach in which responses are sparsely sampled and the missing data are recovered using a computational technique known as The online version contains supplementary material available at 10.1007/s42761-022-00161-2.