arXiv AI
6d ago
FSGR: Mitigating Token Frequency Bias for Fair SID-Based Generative Recommendation
arXiv:2608. 12845v1 Announce Type: cross Abstract: Semantic ID (SID)-based generative recommendation has recently achieved remarkable success.
Fairness Attacks on Recommender Systems
arXiv:2606. 29064v1 Announce Type: cross Abstract: The unfairness of recommender systems has become a topic of concern due to its significant social and ethical implications.
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arXiv Machine Learning
Jun 9
The Injection Paradox: Brand-Level Suppression in Safety-Trained LLM Recommendations via RAG Context Injection
arXiv:2606. 09204v1 Announce Type: new Abstract: We present a reproducible failure mode of safety training in RAG-based LLM recommendation -- the Injection Paradox -- in which prompt injections embedded in retrieved documents backfire against the attacker, suppressing the target brand below the injection-free baseline.
arXiv AI
Jun 12
One Polluted Page Is Enough: Evaluating Web Content Pollution in Generative Recommenders
arXiv:2606. 13610v1 Announce Type: cross Abstract: Search-augmented LLMs increasingly mediate everyday consumer recommendations by retrieving live web content.
arXiv Machine Learning
Jul 23
Evaluating and Mitigating Gender Bias in Pre-trained Embeddings for ML-based Recruitment
arXiv:2607. 20073v1 Announce Type: new Abstract: AI-based recruitment systems that rely on machine learning models trained on historical CV data, risk perpetuating and amplifying social biases.
arXiv Machine Learning
Jun 29
Towards Reliable Recommender Systems for Rating Data
arXiv:2412. 20802v3 Announce Type: replace-cross Abstract: Recommender systems are widely used in the digital landscape to match users with content fitting their preferences.
arXiv AI
Aug 10
Towards a Theoretical Understanding of Two Tower Recommendation Models
arXiv:2403. 00802v2 Announce Type: replace-cross Abstract: Production-grade recommender systems rely heavily on a large-scale corpus used by online media services, including Netflix, Pinterest, and Amazon.
arXiv AI
Jun 2
Perturbation Effects on Accuracy and Fairness among Similar Individuals
arXiv:2404. 01356v3 Announce Type: replace-cross Abstract: Deep neural networks are vulnerable to adversarial perturbations that can simultaneously degrade prediction robustness and individual fairness across diverse application settings.
arXiv Machine Learning
Jul 7
Counterfactual Methods for Detecting Unfairness in Anti-Money Laundering Algorithms
arXiv:2607. 05101v1 Announce Type: new Abstract: The application of machine learning-based predictive algorithms to Anti-Money Laundering (AML) has grown rapidly, driven by the vast volume of financial transaction data available to banks.
arXiv AI
Aug 11
Fair on the Surface? Benchmarking Hidden-Output Fairness Gaps in LLM Recommenders
arXiv:2608. 08284v1 Announce Type: new Abstract: Fairness audits for LLM-based recommenders have largely focused on observable outputs, implicitly assuming that stable recommendations reflect stable internal processing.
arXiv Machine Learning
Jun 16
TCHG: Tri-Trust Conditioned Heterogeneous Graph Learning for Reliable Dynamic Trust Prediction
arXiv:2606. 16611v1 Announce Type: new Abstract: Trust prediction infers latent user-user trust relations and provides important support for social recommendation, fake-review and manipulation detection, and risk identification.
arXiv AI
Jul 28
SafeCRS: Personalized Safety Alignment for LLM-Based Conversational Recommender Systems
arXiv:2603. 03536v2 Announce Type: replace-cross Abstract: Current LLM-based conversational recommender systems (CRS) primarily optimize recommendation accuracy and user satisfaction.
arXiv AI
3d ago
Training Fair Tabular Foundation Models
arXiv:2608. 14211v1 Announce Type: cross Abstract: Tabular Foundation Models (TFMs) have emerged as leading methods for tabular predictive tasks, leveraging in-context learning to predict on new data without task-specific training.