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: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.
arXiv:2608.30333v1 Announce Type: cross Abstract: Next-basket repurchase recommendation is commonly formulated as a ranking task: given a customer's purchase history, the system ranks previously purc...
arXiv:2607. 14161v1 Announce Type: cross Abstract: Pinterest is where people turn inspiration into action as users browse ideas, then take steps toward realization, often by discovering shoppable content.
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.
StoBasket is a joint stochastic basket‑editing framework that simultaneously optimizes upsell and cross‑sell offers for a retail basket. It selects replacement and addition offers, evaluates them by expected incremental contribution margin, and couples the responses through displayed‑offer, accepted‑offer, and joint‑acceptance channels—an approach that, according to simulations on five public basket datasets, captures almost all attainable margin (within 0.5–0.7%). The method demonstrates that correct economic accounting and joint selection can unlock significant margin that traditional popularity‑weighted or next‑basket recommenders leave unrealized, and it provides an exact optimizer that solves real baskets in milliseconds.
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. 19526v1 Announce Type: new Abstract: "Stop Chasing the C-index when Evaluating Survival Analysis Models" (ICML 2026, Spotlight) argued normatively, on synthetic data, that evaluating survival models by discrimination alone, i.
The paper presents a framework for integrating explainable AI into customer churn prediction for telecommunications. It benchmarks four classifiers—Logistic Regression, Random Forest, XGBoost, and LightGBM—on the IBM Telco Customer Churn dataset, finding comparable performance with Logistic Regression achieving the highest AUC-ROC and LightGBM the highest accuracy. Explanations are provided via SHAP and LIME at both global and instance levels, and a four‑layer CRM integration architecture is proposed to translate risk scores and attribution vectors into actionable retention strategies, projecting a 3.3–5.3 percentage point reduction in churn and $199K–$319K savings per campaign cycle.
arXiv:2606. 17931v1 Announce Type: new Abstract: In recent years, electronic (E) commerce services have rapidly increased in the daily lives of people, which helpsthem to purchase products online.
arXiv:2609.16407v1 Announce Type: cross Abstract: On a delivery platform, personalized store ranking greatly influences what users find and order. Unlike digital-only domains, candidate stores are lo...
arXiv:2606. 06776v1 Announce Type: new Abstract: Customer churn prediction is a central task in customer analytics, particularly in non-contractual, pay-per-use service environments where disengagement is not explicitly observed and must be inferred from behavioral inactivity.
MOSS (Model Sanity Service) is a production framework that embeds LLM-based relevance scoring into a continuous monitoring loop for recommendation, search, and offer surfaces, using targeted sampling, strategy-aware prompting, and structured alerting to diagnose semantic failures. It has been deployed for over six months at a large U.S. retailer, identifying two confirmed serving defects and quantifying a 76.4% reduction in alert rate for a backfill blending fix. Benchmark results show that LLM judges outperform non-LLM baselines on both recommendation and search tasks, demonstrating that a single LLM monitoring framework can deliver actionable quality signals across multiple product discovery surfaces.