LLM-HYPER treats an LLM as a hypernetwork that outputs feature-wise weights for a linear CTR model from few-shot multimodal ad examples, achieving 55.9% better NDCG@10 than cold-start baselines and successful production deployment.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
MLTFR combines user-guided token filtering with a multi-LLM mixture-of-experts and Fisher-weighted consensus expert to deliver stable gains in corpus-free sequential recommendation.
A2Gen treats timed user actions on short videos as generative sequences and reports large-scale online gains in watch time, interactions, and retention.
citing papers explorer
-
LLM-HYPER: Generative CTR Modeling for Cold-Start Ad Personalization via LLM-Based Hypernetworks
LLM-HYPER treats an LLM as a hypernetwork that outputs feature-wise weights for a linear CTR model from few-shot multimodal ad examples, achieving 55.9% better NDCG@10 than cold-start baselines and successful production deployment.
-
Multi-LLM Token Filtering and Routing for Sequential Recommendation
MLTFR combines user-guided token filtering with a multi-LLM mixture-of-experts and Fisher-weighted consensus expert to deliver stable gains in corpus-free sequential recommendation.
-
Action-Aware Generative Sequence Modeling for Short Video Recommendation
A2Gen treats timed user actions on short videos as generative sequences and reports large-scale online gains in watch time, interactions, and retention.