arXiv:2608. 03647v1 Announce Type: cross Abstract: Out-of-distribution (OOD) recommendation is vulnerable to preference shifts induced by a latent environment.
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arXiv:2608. 08422v1 Announce Type: cross Abstract: Ranking data arise in scientific and machine learning applications, including recommendation systems, information retrieval, voting, marketing, and AI preference ranking from human feedback.
GenCAR introduces a method for out‑of‑distribution recommendation that balances utility and risk by controlling the proxy‑label false discovery rate (FDR). It frames the problem as an α‑Valid Counterfactual Recommendation (α‑VCR) task, coupling counterfactual supervision with calibrated set selection using conformal p‑values and Benjamini–Hochberg filtering. The approach theoretically bounds counterfactual approximation error and guarantees finite‑sample, distribution‑free FDR control under various dependence assumptions, and empirical results show improved OOD candidate recovery across benchmarks.
arXiv:2511. 07280v5 Announce Type: replace-cross Abstract: Personalized recommendation systems shape much of user choice online, yet their targeted nature makes separating out the value of recommendation and the underlying goods challenging.
arXiv:2606. 04550v1 Announce Type: cross Abstract: E-commerce recommender systems strongly influence which products users consider and purchase, yet sustainability signals such as Product Carbon Footprint (PCF) are almost never available at catalog scale.
The paper introduces PUID, a Personalized Unobserved-Confounding-aware Interaction Deconfounder, designed to mitigate hidden confounding in recommender systems without relying on costly randomized controlled trials. PUID estimates user-item level sensitivity bounds using an entropy-based method that gauges the strength of hidden confounding from the mutual information between observed features and exposure status. An adversarial optimization strategy and a benchmark-guided variant (BPUID) further enhance robustness and predictive accuracy, and experiments on three real-world datasets show consistent outperformance over state-of-the-art baselines.
arXiv:2601. 02322v2 Announce Type: replace-cross Abstract: A common approach to out-of-distribution prediction restricts models to causal or invariant covariates to avoid spurious associations that may change across environments.