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Quantifying Preference Manipulation: Growth Trends in Amazon's Barrier-to-Exit Over Time

by EScholar: Electronic Academic Papers for ScholarsMay 2nd, 2024

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The analysis shows a 1.8% annual growth in Barrier-to-Exit, indicating a significant trend in preference manipulation within Amazon's recommender system. Visualizations illustrate a 43% growth in Barrier-to-Exit over the study period, highlighting shifting user behaviors and preferences.

featured image - Quantifying Preference Manipulation: Growth Trends in Amazon's Barrier-to-Exit Over Time

Authors:

(1) Jonathan H. Rystrøm.

Table of Links

Abstract and Introduction

Conclusions and References

A. Validation of Assumptions

B. Other Models

C. Pre-processing steps

4 Results

The results from the model can be seen in table 1. The coefficient for time is 0.018 (SE=0.001). This implies growth in Barrier-to-Exit of 1.8% per year. This is highly significant (T=29.95, p ≪ 0.0001). The coefficient for activity level is 0.614 (SE=0.001), which is also highly significant (T=450.11, p ≪ 0.0001).

A visual representation of these models can be seen in figure 4. The partial effects plot (Fox, 2003) in fig 4a shows an increase in Barrier-to-Exit from approximately 1.15 to 1.5. This translates into a growth of approximately 43% over the duration of the study. Fig. 4b shows the effect plot with residuals.