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

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

URL: http://arxiv.org/abs/2407.01630v1
Date: Sat, 29 Jun 2024 14:34:32 GMT
Title: A survey on the impact of AI-based recommenders on human behaviours: methodologies, outcomes and future directions
Authors: Luca Pappalardo, Emanuele Ferragina, Salvatore Citraro, Giuliano Cornacchia, Mirco Nanni, Giulio Rossetti, Gizem Gezici, Fosca Giannotti, Margherita Lalli, Daniele Gambetta, Giovanni Mauro, Virginia Morini, Valentina Pansanella, Dino Pedreschi,
Abstract summary: This survey analyses the impact of recommenders in four human-AI ecosystems. Social media, online retail, urban mapping and generative AI ecosystems are studied.
Score: 3.802956917145726
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.

Related papers

A Survey of Latent Factor Models in Recommender Systems [0.0]
This survey systematically reviews latent factor models in recommender systems. The literature is examined through a structured framework covering learning data, model architecture, learning strategies, and optimization techniques.
arXiv Detail & Related papers (2024-05-28T11:28:59Z)
Negotiating the Shared Agency between Humans & AI in the Recommender System [1.4249472316161877]
Concerns about user agency have arisen due to the inherent opacity (information asymmetry) and the nature of one-way output (power asymmetry) on algorithms. We seek to understand how types of agency impact user perception and experience, and bring empirical evidence to refine the guidelines and designs for human-AI interactive systems.
arXiv Detail & Related papers (2024-03-23T19:23:08Z)
Unleashing the Power of AI. A Systematic Review of Cutting-Edge Techniques in AI-Enhanced Scientometrics, Webometrics, and Bibliometrics [1.2374541748245838]
The study aims to analyze the synergy of Artificial Intelligence (AI) with scientometrics, webometrics, and bibliometrics. Our aim is to explore the potential of AI in revolutionizing the methods used to measure and analyze scholarly communication.
arXiv Detail & Related papers (2024-02-22T15:10:02Z)
Embedding in Recommender Systems: A Survey [67.67966158305603]
A crucial aspect is embedding techniques that covert the high-dimensional discrete features, such as user and item IDs, into low-dimensional continuous vectors. Applying embedding techniques captures complex entity relationships and has spurred substantial research. This survey covers embedding methods like collaborative filtering, self-supervised learning, and graph-based techniques.
arXiv Detail & Related papers (2023-10-28T06:31:06Z)
Modeling Recommender Ecosystems: Research Challenges at the Intersection of Mechanism Design, Reinforcement Learning and Generative Models [17.546954143602818]
We argue that modeling the incentives and behaviors of all actors in the system is strictly necessary to maximize the value the system brings to these actors and improve overall ecosystem "health" We propose a conceptual framework that encompasses these elements, and articulate a number of research challenges that emerge at the intersection of these different disciplines.
arXiv Detail & Related papers (2023-09-08T03:20:58Z)
Bias and Fairness in Large Language Models: A Survey [73.87651986156006]
We present a comprehensive survey of bias evaluation and mitigation techniques for large language models (LLMs) We first consolidate, formalize, and expand notions of social bias and fairness in natural language processing. We then unify the literature by proposing three intuitive, two for bias evaluation, and one for mitigation.
arXiv Detail & Related papers (2023-09-02T00:32:55Z)
Fairness in Recommender Systems: Research Landscape and Future Directions [119.67643184567623]
We review the concepts and notions of fairness that were put forward in the area in the recent past. We present an overview of how research in this field is currently operationalized. Overall, our analysis of recent works points to certain research gaps.
arXiv Detail & Related papers (2022-05-23T08:34:25Z)
Reinforcement Learning with Heterogeneous Data: Estimation and Inference [84.72174994749305]
We introduce the K-Heterogeneous Markov Decision Process (K-Hetero MDP) to address sequential decision problems with population heterogeneity. We propose the Auto-Clustered Policy Evaluation (ACPE) for estimating the value of a given policy, and the Auto-Clustered Policy Iteration (ACPI) for estimating the optimal policy in a given policy class. We present simulations to support our theoretical findings, and we conduct an empirical study on the standard MIMIC-III dataset.
arXiv Detail & Related papers (2022-01-31T20:58:47Z)
Heterogeneous Demand Effects of Recommendation Strategies in a Mobile Application: Evidence from Econometric Models and Machine-Learning Instruments [73.7716728492574]
We study the effectiveness of various recommendation strategies in the mobile channel and their impact on consumers' utility and demand levels for individual products. We find significant differences in effectiveness among various recommendation strategies. We develop novel econometric instruments that capture product differentiation (isolation) based on deep-learning models of user-generated reviews.
arXiv Detail & Related papers (2021-02-20T22:58:54Z)
Through the Data Management Lens: Experimental Analysis and Evaluation of Fair Classification [75.49600684537117]
Data management research is showing an increasing presence and interest in topics related to data and algorithmic fairness. We contribute a broad analysis of 13 fair classification approaches and additional variants, over their correctness, fairness, efficiency, scalability, and stability. Our analysis highlights novel insights on the impact of different metrics and high-level approach characteristics on different aspects of performance.
arXiv Detail & Related papers (2021-01-18T22:55:40Z)
A unified survey of treatment effect heterogeneity modeling and uplift modeling [24.803992990503186]
In recent years, a need for estimating the heterogeneous treatment effects conditioning on the different characteristics of individuals has emerged. To meet the need, researchers and practitioners from different communities have developed algorithms. We provide a unified survey of these two seemingly disconnected but closely related approaches under the potential outcome framework.
arXiv Detail & Related papers (2020-07-14T02:16:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.