click-through rate
PulseAugur coverage of click-through rate — every cluster mentioning click-through rate across labs, papers, and developer communities, ranked by signal.
4 day(s) with sentiment data
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MARCO framework improves ad conversion prediction by decomposing click intent
Researchers have developed MARCO, a framework designed to improve ad conversion prediction by decomposing user clicks based on intent. Unlike traditional models that treat all clicks equally, MARCO categorizes clicks by…
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PromptPack cuts LLM annotation costs by 89% for recommendation platforms
Researchers have developed PromptPack, a new system designed to reduce the cost and increase the efficiency of using large-language models (LLMs) for online recommendation platforms. The system addresses the issue of hi…
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New LO-FAR workflow offers cost-effective feature ranking for ad recommendations
Researchers have developed LO-FAR, a new workflow for ranking sparse features in industrial ad recommendation systems. This CPU-only method uses lightweight local estimators to rank features based on their predictive si…
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New RAMP method boosts ad prediction accuracy with limited user data
Researchers have developed a new method called RAMP (Robust Ad Recommendation Under Limited Personalized-Feature Availability via Masking and Alignment Pathways) to improve the accuracy of click-through rate (CTR) and c…
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AI models advance ad headline generation with improved CTR and quality · 2 sources tracked
Two new research papers propose advanced methods for generating advertising headlines. One paper introduces COBART, a controlled, optimized, bidirectional, and auto-regressive Transformer model that uses prefix control …
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New method boosts user modeling by using negative behavior signals
Researchers have developed a new method for sequential user modeling in click-through rate prediction that incorporates implicit negative behaviors, such as skips and scroll-pasts, alongside positive interactions. This …
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New framework tackles ad bias for coupon marketing
Researchers have developed a new framework called UniMVT to address confounding bias in online advertising, particularly for coupon marketing. This model disentangles user preferences from the effects of interventions l…
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Quantum annealers tackle recommender systems with new PDQUBO method
Researchers have developed a new method called PDQUBO for feature selection in recommender systems, designed to run on quantum annealers. This approach directly optimizes for recommender system performance by quantifyin…