Learning user preferences and understanding calendar contexts for event scheduling

Donghyeon Kim, Jinhyuk Lee, Donghee Choi, Jaehoon Choi, Jaewoo Kang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationCIKM 2018 - Proceedings of the 27th ACM International Conference on Information and Knowledge Management
EditorsNorman Paton, Selcuk Candan, Haixun Wang, James Allan, Rakesh Agrawal, Alexandros Labrinidis, Alfredo Cuzzocrea, Mohammed Zaki, Divesh Srivastava, Andrei Broder, Assaf Schuster
PublisherAssociation for Computing Machinery
Pages337-346
Number of pages10
ISBN (Electronic)9781450360142
DOIs
Publication statusPublished - 2018 Oct 17
Event27th ACM International Conference on Information and Knowledge Management, CIKM 2018 - Torino, Italy
Duration: 2018 Oct 222018 Oct 26

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Other

Other27th ACM International Conference on Information and Knowledge Management, CIKM 2018
CountryItaly
CityTorino
Period18/10/2218/10/26

Keywords

  • Convolutional neural network
  • Digital assistant
  • Event scheduling
  • Highway network
  • Multi-agent
  • Preference
  • Recurrent neural network

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

Fingerprint Dive into the research topics of 'Learning user preferences and understanding calendar contexts for event scheduling'. Together they form a unique fingerprint.

  • Cite this

    Kim, D., Lee, J., Choi, D., Choi, J., & Kang, J. (2018). Learning user preferences and understanding calendar contexts for event scheduling. In N. Paton, S. Candan, H. Wang, J. Allan, R. Agrawal, A. Labrinidis, A. Cuzzocrea, M. Zaki, D. Srivastava, A. Broder, & A. Schuster (Eds.), CIKM 2018 - Proceedings of the 27th ACM International Conference on Information and Knowledge Management (pp. 337-346). (International Conference on Information and Knowledge Management, Proceedings). Association for Computing Machinery. https://doi.org/10.1145/3269206.3271712