---
title: "What recommendations do to the people who get them"
description: "Recommender systems appear on 111 recent papers and personalized recommendation on 54, almost all in AI journals; 45 recommendation papers carry a diversity, fairness or popularity-bias keyword, and welfare, consumer surplus and addiction appear in almost none. Recommendation decides what billions of people see and buy. Who gains, and by how much?"
url: https://scholar4ai.com/ideas/recommender-systems-and-welfare
markdown_url: https://scholar4ai.com/ideas/recommender-systems-and-welfare.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["ai", "economics", "platforms"]
keywords: ["recommender systems", "popularity bias", "recommendation diversity"]
---

# What recommendations do to the people who get them

Recommender systems appear on 111 recent papers and personalized recommendation on 54, almost all in AI journals; 45 recommendation papers carry a diversity, fairness or popularity-bias keyword, and welfare, consumer surplus and addiction appear in almost none. Recommendation decides what billions of people see and buy. Who gains, and by how much?

## What prompted this

Since October 2025, 513 papers carry a recommender or recommendation keyword (440 in AI journals): "recommender systems" on 111, "personalized recommendation" on 54, "sequential recommendation" on 47, "multimodal recommendation" on 38. Forty-five of them also carry a filter-bubble, diversity, polarization, fairness, welfare, popularity-bias or well-being keyword, led by "popularity bias" on 19 and "recommendation diversity" on 13.

## Questions

- In platform experiments, how much consumer surplus do recommendations create relative to search alone, and how is it split between consumers, sellers and the platform?
- Do recommendations that optimize engagement reduce the variety of what people consume over months, and does the variety loss show up in satisfaction?
- When platforms are required to offer a non-personalized feed, as the EU now does, who switches, and what changes?

## Data that could help

Platform experiments with randomized recommendation policies, consumption logs over long horizons, and the EU's transparency reports on feed options.

## Next step

Run or obtain a platform experiment that randomizes recommendation strength and measure purchases, variety and self-reported satisfaction over three months.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 23 – Oct 26* | Papers, last 36 months |
|---|---:|---:|---|---:|
| recommender systems | 9 | #149 | 0, 0, 0, 0, 0, 0, 8, 7, 5, 6, 9, 13, 8, 13, 11, 15, 20, 15, 18, 12, 11, 13, 17, 18, 10, 15, 11, 24, 10, 11, 13, 9, 14, 12, 19, 11, 9 | 378 |
| popularity bias | 0 | – | 0, 0, 0, 0, 0, 0, 1, 2, 0, 3, 0, 0, 1, 1, 1, 2, 0, 1, 1, 2, 2, 1, 2, 1, 1, 3, 5, 3, 0, 3, 1, 2, 2, 0, 1, 2, 0 | 44 |
| recommendation diversity | 0 | – | 0, 0, 0, 0, 0, 0, 0, 1, 0, 2, 0, 0, 1, 0, 0, 2, 2, 4, 1, 1, 1, 3, 3, 0, 0, 1, 3, 0, 0, 1, 3, 2, 0, 1, 2, 0, 0 | 34 |

## Cite this page

Scholar4AI. (2026). What recommendations do to the people who get them. Journal Research Rankings. Retrieved October 23, 2026, from https://scholar4ai.com/ideas/recommender-systems-and-welfare

```bibtex
@misc{scholar4ai-ideas-recommender-systems-and-welfare,
  author = {{Scholar4AI}},
  title = {What recommendations do to the people who get them},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/ideas/recommender-systems-and-welfare}},
  urldate = {2026-10-23},
  note = {Journal Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).