Modeling mutual feedback between users and recommender systems

TL;DR

This paper models the long-term mutual feedback between users and recommender systems, revealing that recommendations can lead to increased popularity inequality and reduced diversity over time, despite improving short-term accuracy.

Contribution

It introduces a network evolution model to study the complex dynamics of user-recommender feedback and highlights the long-term effects on item popularity and diversity.

Findings

Recommendations can cause popularity inequality to increase over time.

Long-term recommendation use narrows user choice by favoring popular items.

Reducing short-term accuracy can mitigate adverse effects on diversity.

Abstract

Recommender systems daily influence our decisions on the Internet. While considerable attention has been given to issues such as recommendation accuracy and user privacy, the long-term mutual feedback between a recommender system and the decisions of its users has been neglected so far. We propose here a model of network evolution which allows us to study the complex dynamics induced by this feedback, including the hysteresis effect which is typical for systems with non-linear dynamics. Despite the popular belief that recommendation helps users to discover new things, we find that the long-term use of recommendation can contribute to the rise of extremely popular items and thus ultimately narrow the user choice. These results are supported by measurements of the time evolution of item popularity inequality in real systems. We show that this adverse effect of recommendation can be tamed by sacrificing part of short-term recommendation accuracy.