Uni-AdGen uses a unified autoregressive framework with foreground perception, instruction tuning, and coarse-to-fine preference modules to generate personalized image-text ads from noisy user behaviors, outperforming baselines on a new PAd1M dataset.
arXiv: 2508.18118 [cs.IR]
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TriRec is a two-stage LLM-agent recommender that uses item self-promotion followed by platform-level sequential re-ranking to jointly optimize user utility, item exposure, and exposure fairness.
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Design Your Ad: Personalized Advertising Image and Text Generation with Unified Autoregressive Models
Uni-AdGen uses a unified autoregressive framework with foreground perception, instruction tuning, and coarse-to-fine preference modules to generate personalized image-text ads from noisy user behaviors, outperforming baselines on a new PAd1M dataset.
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Breaking User-Centric Agency: A Tri-Party Framework for Agent-Based Recommendation
TriRec is a two-stage LLM-agent recommender that uses item self-promotion followed by platform-level sequential re-ranking to jointly optimize user utility, item exposure, and exposure fairness.