User response driven content understanding with causal inference

Document Type

Conference Proceeding

Publication Date

11-1-2019

Abstract

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.

Identifier

85078934615 (Scopus)

ISBN

[9781728146034]

Publication Title

Proceedings IEEE International Conference on Data Mining Icdm

External Full Text Location

https://doi.org/10.1109/ICDM.2019.00168

ISSN

15504786

First Page

1324

Last Page

1329

Volume

2019-November

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