With online calendar services gaining popularity worldwide, calendar data has become one of the richest context sources for understanding human behavior. However, event scheduling is still time-consuming even with the development of online calendars. Although machine learning based event scheduling models have automated scheduling processes to some extent, they often fail to understand subtle user preferences and complex calendar contexts with event titles written in natural language. In this paper, we propose Neural Event Scheduling Assistant (NESA) which learns user preferences and understands calendar contexts, directly from raw online calendars for fully automated and highly effective event scheduling. We leverage over 593K calendar events for NESA to learn scheduling personal events, and we further utilize NESA for multi-attendee event scheduling. NESA successfully incorporates deep neural networks such as Bidirectional Long Short-Term Memory, Convolutional Neural Network, and Highway Network for learning the preferences of each user and understanding calendar context based on natural languages. The experimental results show that NESA significantly outperforms previous baseline models in terms of various evaluation metrics on both personal and multi-attendee event scheduling tasks. Our qualitative analysis demonstrates the effectiveness of each layer in NESA and learned user preferences.
|Title of host publication
|CIKM 2018 - Proceedings of the 27th ACM International Conference on Information and Knowledge Management
|Norman Paton, Selcuk Candan, Haixun Wang, James Allan, Rakesh Agrawal, Alexandros Labrinidis, Alfredo Cuzzocrea, Mohammed Zaki, Divesh Srivastava, Andrei Broder, Assaf Schuster
|Association for Computing Machinery
|Number of pages
|Published - 2018 Oct 17
|27th ACM International Conference on Information and Knowledge Management, CIKM 2018 - Torino, Italy
Duration: 2018 Oct 22 → 2018 Oct 26
|International Conference on Information and Knowledge Management, Proceedings
|27th ACM International Conference on Information and Knowledge Management, CIKM 2018
|18/10/22 → 18/10/26
Bibliographical noteFunding Information:
This research was supported by National Research Foundation of Korea (NRF-2017R1A2A1A17069645, NRF-2017M3C4A7065887).
© 2018 Association for Computing Machinery.
- Convolutional neural network
- Digital assistant
- Event scheduling
- Highway network
- Recurrent neural network
ASJC Scopus subject areas
- General Decision Sciences
- General Business,Management and Accounting