AttResRec: Learning User Credibility for Attack Resistant Matrix Factorization Recommendation
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
The pervasive threat of shilling attacks, where malicious users inject fraudulent ratings to manipulate recommendations, critically undermines the reliability of Matrix Factorization (MF)-based recommender systems. This paper proposes AttResRec, a novel MF-based approach designed to improve system integrity by learning and integrating user credibility directly into the recommendation pipeline. AttResRec's defense is built upon three synergistic innovations. First, it employs a user credibility estimation mechanism that quantifies user credibility by assessing the consistency between an individual's interaction history and prevalent item co-occurrence patterns identified from collective user behavior. This directly enables differentiation between genuine and potentially malicious users. Second, the learned credibility dynamically informs a Credibility-aware Huber Loss (CHL) function. The CHL adaptively modifies its error sensitivity, rigorously penalizing deviations for high-credibility users while robustly limiting the influence of large errors associate with low-credibility users. Third, the model optimization is performed via Credibility-Weighted Stochastic Gradient Descent (CW-SGD), ensuring that users with lower credibility scores exert a diminished influence on the learned model parameters. Extensive experiments on the MovieLens-25M and Amazon Musical Instruments datasets, under diverse shilling attack scenarios, demonstrate AttResRec's benefits. That is, it not only achieves superior recommendation accuracy but also exhibits enhanced attack resistance, evidenced by lower prediction shift and hit ratios for poisoned items in poisoned environments, compared to state-of-the-art robust baselines.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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