ROBUREC: Building a Robust Recommender using Autoencoders with Anomaly Detection
Bibliographic record
Abstract
In the realm of social network analysis and mining, recommendation systems have become indispensable algorithms in assisting users and industries in navigating the available contents or products in various domains and getting the most personalized recommendations to their interests and preferences. However, if the input data has been generated by malicious users, that poses a significant challenge to recommender systems' reliability and efficiency. One of the main threats that poses a challenge to recommender systems is shilling attacks. Shilling attacks tend to manipulate or poison the data in the systems' training phase, leading to biased or compromised recommendations. To address this challenge, we propose a robust recommender system using variational autoencoders (VAE) with Anomaly detection. Our model learns complex and non-linear patterns by exclusively focusing on the user-item interaction data, represented by a binary user-item interaction matrix, making it more resilient to classic shilling attacks. Moreover, our paper incorporates an anomaly detection mechanism, alongside the autoencoder, that analyzes the reconstruction errors, i.e. (MSE) between the original interactions and their reconstructed ones. We test the model on a real-world dataset and evaluate it using Recall@k and NDCG@k. This work enhances the trustworthiness and accuracy of recommendation algorithms, mainly when deployed in social network analysis and mining, where the potential for malicious data manipulation is a critical concern.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".