Across three chapters of this doctoral dissertation, I explore how machine learning algorithms can be used to efficiently acquire personal information and responsibly use it in decision making, in marketing and beyond. In the first chapter, I show that machine learning on consumer facial images can reveal a variety of personal information. I provide evidence that such information can be profitably used by marketers.
I also investigate the mechanism behind how facial images reveal personal information. In the second chapter, I propose a new self-supervised deep reinforcement learning approach to question prioritization and questionnaire shortening and show it is competitive against benchmark methods. I use the proposed method to show that typical consumer data sets can be reconstructed well based on relatively small select subsets of their columns. The reconstruction quality grows logarithmically in the relative size of the column subset, implying diminishing returns on measurement.
Thus, many long questionnaires could be shortened with minimal information loss, increasing the consumer research efficiency and enabling previously impossible multi-scale omnibus studies. In the third chapter, I present a method to speed up ranking under constraints for live ethical content recommendations by predicting, rather than finding exactly, the solution to the underlying time-intensive optimization problem. The approach enables solving larger-than-previously-reported constrained content-ranking problems in real time, within 50 milliseconds, as required to avoid the perception of latency by the users. The approach could also help speed up general assignment and matching tasks.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/m0dw-ay87 |
| Date | January 2022 |
| Creators | Tkachenko, Yegor |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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