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
This paper presents GraphJet, a new graph-based system for generating content recommendations at Twitter. As motivation, we trace the evolution of our formulation and approach to the graph recommendation problem, embodied in successive generations of systems. Two trends can be identified: supplementing batch with real-time processing and a broadening of the scope of recommendations from users to content. Both of these trends come together in Graph-Jet, an in-memory graph processing engine that maintains a real-time bipartite interaction graph between users and tweets. The storage engine implements a simple API, but one that is sufficiently expressive to support a range of recommendation algorithms based on random walks that we have refined over the years. Similar to Cassovary, a previous graph recommendation engine developed at Twitter, GraphJet assumes that the entire graph can be held in memory on a single server. The system organizes the interaction graph into temporally-partitioned index segments that hold adjacency lists. GraphJet is able to support rapid ingestion of edges while concurrently serving lookup queries through a combination of compact edge encoding and a dynamic memory allocation scheme that exploits power-law characteristics of the graph. Each GraphJet server ingests up to one million graph edges per second, and in steady state, computes up to 500 recommendations per second, which translates into several million edge read operations per second.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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