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AI and Personalization

SSRN Electronic Journal
2022
  • 11
    Citations
  • 4,194
    Usage
  • 66
    Captures
  • 20
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    11
    • Citation Indexes
      11
  • Usage
    4,194
    • Abstract Views
      2,878
    • Downloads
      1,316
  • Captures
    66
  • Mentions
    20
    • News Mentions
      19
      • 19
    • Blog Mentions
      1
      • Blog
        1
  • Ratings
    • Download Rank
      31,872

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Article Description

This paper reviews the recent developments at the intersection of personalization and AI in marketing and related fields. We provide a formal definition of personalized policy and review the methodological approaches available for personalization. We discuss scalability, generalizability, and counterfactual validity issues and briefly touch upon advanced methods for online/interactive/dynamic settings. We then summarize the three evaluation approaches for static policies -- the Direct method, the Inverse Propensity Score estimator, and the Doubly Robust method. Next, we present a summary of the evaluation approaches for special cases such as continuous actions and dynamic settings. We then summarize the findings on the returns to personalization across various domains, including content recommendation, advertising, and promotions. Next, we discuss the work on the intersection between personalization and welfare. We focus on four of these welfare notions that have been studied in the literature: (1) search costs, (2) privacy, (3) fairness, and (4) polarization. We conclude with a discussion of the remaining challenges and some directions for future research.

Bibliographic Details

Omid Rafieian; Hema Yoganarasimhan

Elsevier BV

Personalization; Machine Learning; AI; Consumer Welfare; Privacy; Fairness; Polarization

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