Beyond Ranking Accuracy: Evaluating LLM-Cited Feature Rationales for Next Basket Repurchase Recommendation
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arXiv:2606. 04387v1 Announce Type: cross Abstract: Sales lead conversion in high-stakes domains (e.
arXiv:2606. 02004v1 Announce Type: cross Abstract: Consumer-price measurement increasingly draws on alternative data sources -- scanner, web-scraped, and transaction/receipt data.
arXiv:2607. 23647v1 Announce Type: cross Abstract: Large language models (LLMs) can summarize heterogeneous user evidence in natural language, but current LLM recommenders often collapse enduring preferences, transient intent, and exposure-induced behavior into one profile.
The paper proposes replacing multiple horizon‑specific binary classifiers with a single survival model to predict time‑to‑repurchase in grocery e‑commerce. Empirical analysis shows a slightly decreasing hazard (k≈0.9) and that a Log‑Normal model best fits marginal distributions while Weibull best fits residuals. A single Accelerated Failure Time (AFT) model matches or surpasses per‑horizon classifiers with fewer trees, and a 4‑parameter calibration maps survival CDFs to horizon probabilities without monotonicity violations, revealing a trade‑off between calibration and ranking within the AFT family.
arXiv:2607. 06993v1 Announce Type: new Abstract: Customer behavior modeling underpins recommendation, marketing, and decision support, yet existing approaches either optimize predictive accuracy without explaining decisions or simulate users without grounding them in real behavioral data.
arXiv:2606. 06779v1 Announce Type: cross Abstract: In multi-vertical e-commerce platforms like DoorDash, relatively newer product verticals such as grocery and retail present a significant opportunity for personalization innovation.