An efficient privacy-preserving point-of-interest recommendation model based on local differential privacy
Bibliographic record
Abstract
Abstract With the rapid development of point-of-interest (POI) recommendation services, how to utilize the multiple types of users’ information safely and effectively for a better recommendation is challenging. To solve the problems of imperfect privacy-preserving mechanism and insufficient response-ability to complex contexts, this paper proposes a hybrid POI recommendation model based on local differential privacy (LDP). Firstly, we introduce randomized response techniques k-RR and RAPPOR to disturb users’ ratings and social relationships, respectively and propose a virtual check-in time generation method to deal with the issue of missing check-in time after disturbance. Secondly, for simultaneously combining multiple types of information, we construct a hybrid model containing three sub-models. Sub-model 1 considers the effect of user preference, social relationship, forgetting feature, and check-in trajectory on similarity calculation. Sub-model 2 analyzes the geographical correlation of POIs. Sub-model 3 focuses on the categories of POIs. Finally, we generate the recommendation results. To test the performance of privacy-preserving and recommendation, we design three groups of experiments on three real-world datasets for comprehensive verifying. The experimental results show that the proposed method outperforms existing methods. Theoretically, our study contributes to the effective and safe usage of multidimensional data science and analytics for privacy-preserving POI recommender system design. Practically, our findings can be used to improve the quality of POI recommendation services.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".