LLM-HYPER: Generative CTR Modeling for Cold-Start Ad Personalization via LLM-Based Hypernetworks

Luyi Ma; Wanjia Sherry Zhang; Zezhong Fan; Shubham Thakur; Kai Zhao; Kehui Yao; Ayush Agarwal; Rahul Iyer

cs.AIApr 13, 2026

LLM-HYPER: Generative CTR Modeling for Cold-Start Ad Personalization via LLM-Based Hypernetworks

Authors: Luyi Ma, Wanjia Sherry Zhang, Zezhong Fan, Shubham Thakur, Kai Zhao, Kehui Yao, Ayush Agarwal, Rahul Iyer,

Organizations: Walmart Global Tech Sunnyvale, California, USA

Abstract

On online advertising platforms, newly introduced promotional ads face the cold-start problem, as they lack sufficient user feedback for model training. In this work, we propose LLM-HYPER, a novel framework that treats large language models (LLMs) as hypernetworks to directly generate the parameters of the click-through rate (CTR) estimator in a training-free manner. LLM-HYPER uses few-shot Chain-of-Thought prompting over multimodal ad content (text and images) to infer feature-wise model weights for a linear CTR predictor. By retrieving semantically similar past campaigns via CLIP embeddings and formatting them into prompt-based demonstrations, the LLM learns to reason about customer intent, feature influence, and content relevance. To ensure numerical stability and serviceability, we introduce normalization and calibration techniques that align the generated weights with production-ready CTR distributions. Extensive offline experiments show that LLM-HYPER significantly outperforms cold-start baselines in NDCG $@10$ by 55.9%. Our real-world online A/B test on one of the top e-commerce platforms in the U.S. demonstrates the strong performance of LLM-HYPER, which drastically reduces the cold-start period and achieves competitive performance. LLM-HYPER has been successfully deployed in production.

Figures & tables

Figure 1: LLM-HYPER: (a) framework and (b)-(d) prompt details. Weights are normalized for CTR prediction.

Model	AUC	NDCG5	NDCG10	L(ms)
Emb T5	0.4105	0.0174	0.0262	0.357
LLM-R	0.0571	0.00931	0.0221	$2.13\mbox{{E}}{3}$
LLM-TR	0.0576	0.00886	0.0508	$3.78\mbox{{E}}{3}$
LH 4o	0.6539	0.0490	0.0764	0.163
LH 5.1	0.6497	0.0427	0.0668	0.158
LH 2.5F	0.6654	0.0435	0.0779	0.160

Table 1: Offline Cold-start Ranking Comparison.

Models	NDCG5	NDCG10
LH 2.5P , zero-shot	0.0376	0.0677
LH 2.5P , zero-shot, $-img$	0.0165	0.0352
LH 2.5P , 3-shot	0.0421	0.0798
LH 2.5P , 3-shot, $-img$	0.0267	0.0511
LH 2.5P , 5-shot	0.0456	0.0792
LH 2.5P , 5-shot, $-img$	0.0292	0.0506

Table 2: CoT and Visual Feature Impact

Figure 3: Explainability Results of Cold Start Model

Figure 4: Counterfactual Robustness of Cold Start Model with accuracy of weight change

Models	Relative CTR score
LLM-HYPER	92%
LR warm	100%

Table 3: Online 30-Day A/B Test Results.

Appendix figures & tables3 assets

Supplementary material from the paper’s appendix.

Appendix

Models	Generation Time(s)
GPT-4o	47.7
GPT-5.1	58.7
Gemini-2.5-Flash	83.3
Gemini-2.5-Pro	102.9

Appendix

Table 4: Offline LLM weight generation time (averaged by each ad)

Ad	Ad Content	Weight	Key Signals	LLM Rationale
Ad 1	Age-appropriate building toys and construction sets	0.95	• Strong: Toy and game category engagement • Strong: Building and construction toy affinity	The dominant feature activation centers on toy and building set interactions. The Ad’s product category and thematic content closely align with this feature’s predictive pattern, indicating strong relevance for engagement prediction.
Ad 2	High-value outdoor furniture with minor recreational overlap	-0.35	• Strong: Indoor recreational products • Weak: Outdoor lawn activities	The dominant feature signal centers on indoor play items. The secondary outdoor activity signal is insufficient to predict interest in premium outdoor living categories.
Ad 3	Children’s footwear (Crocs) with shared toy purchaser demographic	0.0	• Weak: Children’s product category overlap • Weak: Parent shopper demographic	The feature captures toy purchasing behavior, which correlates with parents who also purchase children’s apparel and footwear. The cross-category purchasing pattern creates a neutral signal for children’s fashion items despite product category mismatch.

Appendix

Table 5: Feature weight predictions for feature_toys_games feature across different ad contexts.

Original Ad	Target Features	Mod Type	Modified Ad	$S_{\text{orig}}$	$S_{\text{cf}}$	$\Delta$ Score
Patio & Garden	outdoor activity & sports	Enhanced	Game Day Upgrades: Patio & Grill	0.192	0.735	$+0.543$
Vitamins & Supps	Healthy Living	Neutralized	Daily Essentials	1.055	0.975	$-0.080$
Fall Home Trends	Home Decor	Diminished	Fall Gutter & Roof Maintenance	3.750	1.150	$-2.600$

Appendix

Table 6: Examples of counterfactual semantic modifications and the resulting weight changes generated by LLM-HYPER ( LH 2.5P ).

CTR Prediction

Click-through rate (CTR) prediction estimates which ads or items users will click to improve ranking, recommendation, and advertising decisions.

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Abstract

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