Large-scale User Preference Tracking via Asynchronous and Asymmetric Updating at Twitter
Bibliographic record
Abstract
For content recommendation systems on social media platforms, timely, efficient, and accurate estimation of user preferences can effectively improve their performance and enhance the platforms’ activeness. However, efficiency and accuracy are often a pair of trade-offs; Accurate user preference estimation often requires tracking dynamic preference shifting with complex sequential modelling, whereas efficient systems may fail to follow the shift because of a lack of modelling capacity. This paper presents Asynchronous and Asymmetric User Preference Updating System AAUPU, a distributed collaborative filtering system, that can track hundreds of millions of users’ preferences in real time by processing streaming data. The AAUPU system finds a good balance between efficiency and accuracy, making it well-suited for large-scale personalization service needs on social media platforms. We implemented the system on the Google Cloud Platform and successfully tracked user preference for a population of 400 million active Twitter accounts. To evaluate the estimation quality, we conducted massive A/B tests on the two most important service surfaces of Twitter, involving more than ten million users. Our experimental results show that the recommender system based on the AAUPU system significantly improves the overall recommendation performance on the platform.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".