Implicit user modeling in group chat

Anat Hashavit, Naama Tepper, Inbal Ronen, Lior Leiba, Amir D.N. Cohen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

In recent years, enterprise group chat collaboration tools such as Slack, IBM's Watson Workspace and Microsoft Teams, have presented unprecedented growth. With all the potential benefits of these tools-productivity increase and improved group communication-come significant challenges. Specifically, users find it hard to focus their attention on content that is relevant to them due to the load of conversational content. This load can be handled by personalized content presentation and summarization mitigated by user profiling. We present an unsupervised approach for implicitly modeling group chat users through a combination of a probabilistic topic model and social analysis. We evaluate our approach by testing it on a task of conversation participation prediction, serving as a proxy for anticipating user interests, and show that by utilizing our approach, a system successfully predicts users participation in conversations. We further analyze the contribution of the various user model components and show them to be significant.

Original languageEnglish
Title of host publicationUMAP 2018 - Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization
PublisherAssociation for Computing Machinery, Inc
Pages275-280
Number of pages6
ISBN (Electronic)9781450357845
DOIs
StatePublished - 2 Jul 2018
Externally publishedYes
Event26th ACM International Conference on User Modeling, Adaptation and Personalization, UMAP 2018 - Singapore, Singapore
Duration: 8 Jul 201811 Jul 2018

Publication series

NameUMAP 2018 - Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization

Conference

Conference26th ACM International Conference on User Modeling, Adaptation and Personalization, UMAP 2018
Country/TerritorySingapore
CitySingapore
Period8/07/1811/07/18

Bibliographical note

Publisher Copyright:
© 2018 Association for Computing Machinery.

Keywords

  • Group chat
  • Summarization
  • Unsupervised learning

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