Learning User Preferences and Understanding Calendar Contexts for Event Scheduling
Table 5. NESA Model Ablation (Diff. %: average performance difference % of 4 metrics)
['Model', 'Recall@1', 'Recall@5', 'MRR', 'IEuc', 'Diff. %']
NESA Model Ablation and Analysis To analyze the architecture of NESA, we removed each layer or component of NESA. When the Context layer is removed, the Output layer receives only the intention representation. We feed the title representation instead of the intention representation to the Output layer when the Intention layer is removed. The Context layer has the most significant impact on the overall performance. The Intention layer also shows that incorporating user and duration attributes with title attributes is crucial for event scheduling. The character embedding has substantial effects on the performance. As the number of pre-registered events grows, overall performance improves. Note that the sampling proportion decreases as the number of pre-registered events increases, which causes a high variance in performance.