Bibliographic record
Abstract
Twitter is one of the most popular Online Social Networks (OSNs) nowadays. Twitter users retrieve information from other users by subscribing to their tweets. Twitter users, especially those who have many followees, may receive hundreds or even thousands of tweets daily. Currently, all tweets are shown to users in chronological order. Consequently, a Twitter user may accidentally overlook useful and interesting tweets because the user is overwhelmed by the huge volume of uninteresting tweets. Researchers in the recommendation system community have proposed using recommendation techniques such as collaborative filtering to predict users' preference of tweets and highlight those tweets in which users are most likely to be interested. At the same time, while OSNs such as Twitter have enabled people to conveniently share information and interact with each other online, OSN users are getting increasingly concerned about their online privacy. Researchers in the security community have proposed using techniques such as encrypted tweets to protect users' privacy. In this paper, we propose a privacy-preserving personalized tweet recommendation framework, pTwitterRec, in a Twitter-like social network where users' tweets are hidden from the OSN provider. pTwitterRec provides users with personalized tweet recommendations while keeping users' tweets and interests hidden from the OSN provider as well as other unauthorized entities. pTwitterRec splits the tweet recommendation task between the provider and a semi-trusted third party, so that neither can derive users' sensitive information alone while working together to provide users with personalized tweet recommendations. We implement a prototype and demonstrate through evaluation that pTwitterRec incurs tolerable overhead on today's smartphones.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.097 | 0.096 |
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".