Privacy and security in recommenders: an analytical review
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
Recommender systems (RSs) effectively curb information overload by providing personalized suggestions of items to users across different online domains. Their widespread use in e-commerce enhances user engagement, personalizes shopping experiences, and drives sales growth. However, despite the effectiveness of these systems at generating recommendations for users, they still raise major privacy and security concerns as their data could be exploited for malicious purposes, which can lead to data breaches and misuse. Therefore, this paper presents a comprehensive and systematic review of the underlying causes of privacy and security challenges in RS. It also provides a detailed taxonomy categorizing these concerns based on their targets and the risks they create. It further presents potential solutions that have been used in the literature while identifying challenges and possible research directions to pursue in a bid to address privacy and security concerns in RSs. This paper will be a useful resource for current and upcoming researchers in the domain of RSs. It will support knowledge advancement and steer appropriate research directions.
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 | 0.002 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.012 | 0.019 |
| 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