AttResRec: Learning User Credibility for Attack Resistant Matrix Factorization Recommendation
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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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".