User response driven content understanding with causal inference

Fei Tan, Zhi Wei, Abhishek Pani, Zhenyu Yan

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

2 Scopus citations


Content understanding with many potential industrial applications, is spurring interest by researchers in many areas in artificial intelligence. We propose to revisit the content understanding problem in digital marketing from three novel perspectives. First, our problem is to explore the way how user experience is delivered with divergent key multimedia elements. Second, we treat understanding as to elucidate their causal implications in driving user responses. Third, we propose to understand content based on observational audience visit logs. To approach this problem, we measure and generate heterogeneous content features and model them as binary, multivalued or continuous genres. Multiple key performance indicators (KPIs) are introduced to quantify user responses. We then develop a flexible and adaptive doubly robust estimator to identify the causality between these features and user responses from observational data. The comprehensive experiments are performed on real-world data sets. We show that the further analysis of the experimental results can shed actionable insights on how to improve KPIs. Our work will benefit content distribution and optimization in digital marketing.

Original languageEnglish (US)
Title of host publicationProceedings - 19th IEEE International Conference on Data Mining, ICDM 2019
EditorsJianyong Wang, Kyuseok Shim, Xindong Wu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781728146034
StatePublished - Nov 2019
Event19th IEEE International Conference on Data Mining, ICDM 2019 - Beijing, China
Duration: Nov 8 2019Nov 11 2019

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786


Conference19th IEEE International Conference on Data Mining, ICDM 2019

All Science Journal Classification (ASJC) codes

  • General Engineering


  • Causal Inference
  • Content Understanding
  • Digital Marketing
  • User Engagement


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