A survey on the impact of AI-based recommenders on human behaviours: methodologies, outcomes and future directions

2024-06-29

arXiv.org

AI Summary

This paper addresses the fragmentation of terminology and weak cross-disciplinary comparability in research on recommender system impacts. We conduct a qualitative systematic review spanning four ecosystems: social media, online retail, urban mapping, and generative AI. Drawing on 144 interdisciplinary studies, we propose the first integrative taxonomy: (1) methodologically, distinguishing empirical, simulation-based, observational, and controlled approaches; (2) in impact outcomes, categorizing nine sociotechnical effects—including filter bubbles, model collapse, and polarization; and (3) in analytical granularity, stratifying analysis across individual, item, model, and system levels. This taxonomy unifies conceptual frameworks, exposes both convergences and divergences in behavioral impacts across ecosystems, and establishes a measurable, cross-context benchmark for academic research, policy evaluation, and industry practice.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Application Domains: Humanities & Computational Social Science
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• User Modeling, Personalization and Recommendation: Social recommender systems and personalization for the social Web
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Recommendation systems and assistants (in short, recommenders) are ubiquitous in online platforms and influence most actions of our day-to-day lives, suggesting items or providing solutions based on users' preferences or requests. This survey analyses 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. We follow the customary steps of qualitative systematic review, gathering 144 articles from different disciplines to develop a parsimonious taxonomy of: methodologies employed (empirical, simulation, observational, controlled), outcomes observed (concentration, model collapse, diversity, echo chamber, filter bubble, inequality, polarisation, radicalisation, volume), and their level of analysis (individual, item, model, and systemic). We systematically discuss all findings of our survey substantively and methodologically, highlighting also potential avenues for future research. This 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.

Problem

• Systematically reviews impacts of recommender systems across human-AI ecosystems
• Develops a taxonomy to unify fragmented terminologies in the field
• Analyzes methodologies, outcomes, and levels of analysis from 154 articles

Innovation

• Systematic review of recommender impacts across ecosystems
• Taxonomy of methodologies, outcomes, and analysis levels
• Cross-disciplinary synthesis to unify fragmented terminologies