A Comparative Survey Of Algorithmic Feed Recommendation System Designs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Social media platforms are highly reliant on algorithmic feed systems to deliver content to users. Unlike content recommender systems typically studied in academia, recommendation algorithms for social media feeds are multi-stakeholder and designed to maximize usage, rather than relevance or affinity. How feed algorithms are designed and exactly what content is recommended to users has come under increasing scrutiny from the public and lawmakers. Companies have responded to this scrutiny with more transparency around their systems, including their recommendation algorithms. To aid in comparisons of these newly-transparent systems, we perform a survey of social media feed algorithm systems by conducting a qualitative document analysis of primary source documents. Our survey identifies salient design choices that different apps have made, and algorithm traits that result from those design choices. The key areas of our survey are feed content inventory selection, features used for ranking and four key algorithm traits, along with metrics that capture those traits. We also perform a case study of X’s recently open-sourced feed algorithm, with a particular focus on the key characteristics and algorithm traits identified in our larger survey.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it