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

Written by escholar | Published 2024/05/02
Tech Story Tags: recommendation-systems | user-preference-manipulation | ml-algorithms | user-behavior-analytics | amazon-book-recommendations | barrier-to-exit-analysis | surveillance-capitalism | ethical-ai

TLDRThe 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.via the TL;DR App

Authors:

(1) Jonathan H. Rystrøm.

Table of Links

Abstract and Introduction

Previous Literature

Methods and Data

Results

Discussions

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.

This paper is available on arxiv under CC 4.0 license.


Written by escholar | We publish the best academic work (that's too often lost to peer reviews & the TA's desk) to the global tech community
Published by HackerNoon on 2024/05/02