Empirical Insights into Preference Manipulation

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

Too Long; Didn't Read

Examining both MovieLens and Amazon's recommender systems, this paper provides empirical evidence on preference manipulation trends. It expands on previous studies, highlighting the importance of analyzing diverse user groups and content metrics to understand the nuances of preference shaping in commercial and non-commercial platforms.

featured image - Empirical Insights into Preference Manipulation

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

2 Previous Literature

Several papers have highlighted the need to protect human autonomy and preferences in recommender systems (Calvo et al., 2020; Varshney, 2020; Jannach & Adomavicius, 2016). However, these focus more on the normative need to do so, rather than an empirical analysis of the phenomenon. With this paper, we aim to fill this gap in the literature.

Most previous empirical analysis of preference manipulation in recommender systems has focused on the MovieLensdataset (Harper & Konstan, 2016). MovieLens is a movie recommendation platform developed and maintained by the University of Minnesota.[1] The advantages of this dataset are that it is a) rigorously documented as it is maintained by an academic group; b) freely available; and c) well-structured, thus making analysis easier. However, because it is a non-commercial project it is not susceptible to surveillance capitalistic imperatives to the same extent as e.g. Amazon (Zuboff, 2019).

Nguyen et al. (2014) analysed whether MovieLens users were exposed to less diverse content over time - a type of preference manipulation. While they found a significant (albeit small) decrease, the effect was smaller for users who followed the recommendations than the users who did not. However, as discussed by Rakova and Chowdhury (2019), they focus their analysis on highly-active and highly-nonactive users neglecting the middle. Also, content diversity is an important but incomplete measure of preference manipulation.

Rakova and Chowdhury (2019) also use the MovieLens dataset to define and showcase Barrier-to-Exit. However, their paper is more of a proof-of-concept rather than an analysis. The present paper expands on Rakova and Chowdhury (2019) by applying the metric to a real-world dataset of a commercial recommender system.

[1] movielens.org