Learning User Preferences and Understanding Calendar Contexts for Event Scheduling
Table 4. Multiple Attendee Event Scheduling Results
['Model', 'Recall@1', 'Recall@5', 'MRR', 'IEuc']
5.3.2. Multi-Attendee Event Scheduling Compared to the performances on personal event scheduling, Recall@1 and Recall@5 of RF have been greatly improved, but MRR and IEuc of RF have been degraded. This verifies the limited effectiveness of decision tree based models as reported in the work of Mitchell et al. RF fails to provide precise probability distribution over time intervals, that reflects user preferences and calendar contexts, as MRR and IEuc are more sensitive to suggestion quality over the whole week. Other baseline models such as SVM, LogReg, and MLP have failed to produce meaningful results on multi-attendee event scheduling. We found that the huge performance degradation of these models comes from generalization failure on unseen users as most users (217 out of 260) in the multi-attendee event scheduling dataset are unseen during training on the personal event scheduling dataset. The performance of SVM, LogReg, and MLP on multi-attendee event scheduling was higher (but still insufficient compared to RF and NESA) when all the attendees were comprised of seen users during training. NESA does not suffer from the unseen user problem by understanding raw online calendars to infer user preferences and understand calendar contexts. While preferences of known users can be encoded in user embeddings in NESA, preferences of unseen users can be inferred from their raw calendars. As with the personal event scheduling task, NESA outperforms the other baseline models by large margins on the multi-attendee event scheduling task. Specifically, NESA outperforms the best baseline model RF by 51.2%, 6.0%, 135.0%, and 23.5% in terms of Recall@1, Recall@5, MRR, and IEuc, respectively. This shows that using raw calendar data for understanding user preferences and calendar contexts is very important in event scheduling tasks.