arXiv AI
3d ago
Always-On Experimentation
arXiv:2609.38695v1 Announce Type: cross Abstract: Generative AI has dramatically accelerated the rate at which new treatments---from novel pharmaceuticals to online marketing campaigns---can be conce...
Estimating the Effect of Timing on Coupon Effectiveness
arXiv:2606. 30664v1 Announce Type: cross Abstract: The coupon incentive is one of the most common tools marketers use to court users to engage with a business at various stages of the customer life cycle.
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arXiv AI
4d ago
Challenges and Solutions for Bandits in the Wild: Warm-Started Mixture Bandits for Cross-Cohort Slate Recommendation
arXiv:2609.37800v1 Announce Type: cross Abstract: Many recommender services repeatedly encounter cold-start cohorts, where new users arrive with little or no interaction history. This creates two cha...
arXiv Machine Learning
Aug 31
Budget-Constrained Causal Bandits: Bridging Uplift Modeling and Sequential Decision-Making
The paper introduces Budget-Constrained Causal Bandits (BCCB), an online framework that learns individual treatment effects, explores uncertain users, and manages budget pacing simultaneously. It derives a per-arrival decision rule from a KKT condition of a Lagrangian relaxation, providing a principled algorithmic foundation. Experiments on the Criteo Uplift dataset show BCCB outperforms offline pipelines and other online baselines, especially when historical data is scarce (below 7,500 observations).
arXiv Machine Learning
Jun 2
Jointly Optimizing Debiased CTR and Uplift for Coupons Marketing: A Unified Causal Framework
arXiv:2602. 12972v2 Announce Type: replace-cross Abstract: In online advertising, marketing interventions such as coupons introduce significant confounding bias into Click-Through Rate (CTR) prediction.
arXiv Machine Learning
Aug 13
Certifying What Helps Customer-Return Timing: A Screen-and-Confirm Test for Conditioning Signals, and Why Decay Is Nearly Enough
arXiv:2608. 11555v1 Announce Type: new Abstract: Practitioners enrich customer-return models with ever more signals (lifetime value, category, recency/frequency, calendar, geography), and the temporal-point-process (TPP) literature follows suit with covariate- and external-covariate-conditioned intensities.
arXiv AI
Aug 11
ComboShoppingBench: Evaluating LLM Agents for Budget-Constrained Basket Shopping with Coupons
arXiv:2608. 09282v1 Announce Type: new Abstract: Real-world shopping often requires constructing a basket of complementary items rather than retrieving a single product.
arXiv Machine Learning
Jun 10
Data-Driven Dynamic Assortment in Online Platforms: Learning about Two Sides
arXiv:2606. 11118v1 Announce Type: new Abstract: We study a dynamic assortment problem on a two-sided service platform with incomplete information and heterogeneous customers in a discrete-time setting.
Hugging Face Trending Papers
Aug 27
Incremental Recommendation via Causal Models
The paper proposes an incremental recommendation approach that uses a causal model built from existing holdback data to avoid delivering redundant recommendations. By applying a dual‑threshold targeting policy, the system only recommends content when the likelihood of a treated stream is high and the likelihood of an organic stream is low, thereby reducing recommendation impressions by 7% without hurting overall consumption. Joint training with holdback data also improves the calibration of the treated head, suggesting that causal models capture more generalisable representations than purely observational models.
arXiv Machine Learning
Aug 28
Incremental Recommendation via Causal Models
The paper proposes an incremental recommendation system that uses causal modeling to avoid delivering redundant recommendations. By leveraging existing holdback data and a dual‑threshold targeting policy, the authors reduce recommendation impressions by 7% without harming overall content consumption. Joint training with holdback data also improves the calibration of the treated model, suggesting better generalisable representations than purely observational models.
arXiv AI
Jul 24
Benchmarking the Personalization Capabilities of Large Language Models
arXiv:2607. 20471v1 Announce Type: new Abstract: Personalization, the act of varying a message to induce action from a specific receiver while keeping sender, channel, and time fixed, has a long tradition in psychology and marketing as a two-party problem in which sender and receiver have independent objectives.
arXiv Machine Learning
Jun 8
A Rolling-Window Framework for Churn Prediction and Behavioral Driver Identification
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.
arXiv Machine Learning
2d ago
Robust Budget Pacing with a Single Sample
arXiv:2302.02006v2 Announce Type: replace Abstract: Major Internet advertising platforms offer budget pacing tools as a standard service for advertisers to manage their ad campaigns. Given the inhere...