A survey on the impacts of recommender systems on users, items, and human-AI ecosystems
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908703925469184 |
|---|---|
| author | Pappalardo, Luca Citraro, Salvatore Cornacchia, Giuliano Nanni, Mirco Pansanella, Valentina Rossetti, Giulio Gezici, Gizem Giannotti, Fosca Lalli, Margherita Mauro, Giovanni Barlacchi, Gabriele Gambetta, Daniele Morini, Virginia Pedreschi, Dino Ferragina, Emanuele |
| author_facet | Pappalardo, Luca Citraro, Salvatore Cornacchia, Giuliano Nanni, Mirco Pansanella, Valentina Rossetti, Giulio Gezici, Gizem Giannotti, Fosca Lalli, Margherita Mauro, Giovanni Barlacchi, Gabriele Gambetta, Daniele Morini, Virginia Pedreschi, Dino Ferragina, Emanuele |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_01630 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A survey on the impacts of recommender systems on users, items, and human-AI ecosystems Pappalardo, Luca Citraro, Salvatore Cornacchia, Giuliano Nanni, Mirco Pansanella, Valentina Rossetti, Giulio Gezici, Gizem Giannotti, Fosca Lalli, Margherita Mauro, Giovanni Barlacchi, Gabriele Gambetta, Daniele Morini, Virginia Pedreschi, Dino Ferragina, Emanuele Information Retrieval Artificial Intelligence Computers and Society Human-Computer Interaction 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. |
| title | A survey on the impacts of recommender systems on users, items, and human-AI ecosystems |
| topic | Information Retrieval Artificial Intelligence Computers and Society Human-Computer Interaction |
| url | https://arxiv.org/abs/2407.01630 |