A survey on the impacts of recommender systems on users, items, and human-AI ecosystems

TL;DR

This survey comprehensively reviews the influence of recommendation systems across various human-AI ecosystems, categorizing methodologies and outcomes to facilitate understanding and future research in this fast-evolving field.

Contribution

It provides a systematic taxonomy of recommender system impacts, methodologies, and analysis levels across four ecosystems, addressing terminological fragmentation in the literature.

Findings

Identified key methodologies: empirical, simulation, observational, controlled.

Cataloged outcomes: concentration, diversity, polarization, echo chambers.

Analyzed impacts at individual, item, and ecosystem levels.

Abstract

Recommendation systems and assistants (in short, recommenders) influence through online platforms most actions of our daily lives, suggesting items or providing solutions based on users' preferences or requests. This survey systematically reviews, categories, and discusses the impact of recommenders in four human-AI ecosystems -- social media, online retail, urban mapping and generative AI ecosystems. Its scope is to systematise a fast-growing field in which terminologies employed to classify methodologies and outcomes are fragmented and unsystematic. This is a crucial contribution to the literature because terminologies vary substantially across disciplines and ecosystems, hindering comparison and accumulation of knowledge in the field. We follow the customary steps of qualitative systematic review, gathering 154 articles from different disciplines to develop a parsimonious taxonomy of methodologies employed (empirical, simulation, observational, controlled), outcomes observed (concentration, content degradation, discrimination, diversity, echo chamber, filter bubble, homogenisation, polarisation, radicalisation, volume), and their level of analysis (individual, item, and ecosystem). We systematically discuss substantive and methodological commonalities across ecosystems, and highlight potential avenues for future research. The survey is addressed to scholars and practitioners interested in different human-AI ecosystems, policymakers and institutional stakeholders who want to understand better the measurable outcomes of recommenders, and tech companies who wish to obtain a systematic view of the impact of their recommenders.