Get to the bottom: causal analysis for user modeling

Shi Zong, Branislav Kveton, Shlomo Berkovsky, Azin Ashkan, Zheng Wen

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

1 Citation (Scopus)


Weather affects our mood and behavior, and through them, many aspects of our life. When it is sunny, people become happier and smile, but when it rains, some get depressed. Despite this evidence and the abundance of weather data, weather has mostly been overlooked in the machine learning and data science research. This work shows how causal analysis can be applied to discover the effects of weather on TV watching patterns and how it can be applied for user modeling. We make several contributions. First, we show that some weather attributes, e.g., pressure and precipitation, cause significant changes in TV watching patterns. Second, we compare the results obtained for different levels of user granularity and different types of users. This showcases that causal analysis can be a valuable tool in user modeling. To the best of our knowledge, this is the first large-scale causal study of the impact of weather on TV watching patterns.
Original languageEnglish
Title of host publicationUMAP '17, Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Number of pages9
ISBN (Electronic)9781450346351
Publication statusPublished - 2017
Externally publishedYes
Event25th International Conference on User Modeling, Adaptation, and Personalization, UMAP 2017 - Bratislava, Slovakia
Duration: 9 Jul 201712 Jul 2017


Conference25th International Conference on User Modeling, Adaptation, and Personalization, UMAP 2017


  • weather
  • causal analysis
  • user modeling
  • Weather
  • Causal analysis
  • User modeling


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