Learning User Preferences and Understanding Calendar Contexts for Event Scheduling
Table 3. Personal Event Scheduling Results
['Model', 'Recall@1', 'Recall@5', 'MRR', 'IEuc']
Personal Event Scheduling The reported scores are average test set scores after ten separate trainings. The best scores are in bold. We first see that the performance ranking of the IEuc scores is similar to that of other metric scores such as the Recall@5 scores. This shows that the more a model accurately predicts an answer, the more it suggests nearby time slots around the correct answer. Among the baseline models, MLP performed the best on average, and RF achieved the lowest overall scores. However, despite MLP’s deeper structure, performance improvements of MLP over LogReg were marginal, which shows the limitation of feature based models. NESA achieved higher scores than the baseline models in all metrics by learning to schedule directly using raw calendar data. NESA outperformed the baseline models by 29.6% on average in terms of MRR. More specifically, NESA outperformed MLP, which is the best baseline model, by 36.5%, 19.6%, 20.7%, and 5.7% in terms of Recall@1, Recall@5, MRR, and IEuc, respectively.