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Warm Up Cold-start Advertisements: Improving CTR Predictions Via Learning To Learn ID Embeddings.pdf

1904.11547.pdf
Preview of Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings
🔗 Source: arxiv.org
📊 Size: 854 KB
👤 Author: Feiyang Pan, Shuokai Li, Xiang Ao, Pingzhong Tang, and Qing He
⬇️ Downloads: 113

Summary

Click-through rate (CTR) prediction is crucial in online advertising, and embedding techniques have improved CTR prediction accuracies. However, these techniques are data demanding and struggle with new ads that have little logging data, known as the cold-start problem. To address this, a meta-learning approach called Meta-Embedding is proposed, which learns to generate initial embeddings for new ad IDs. The method trains an embedding generator using previously learned ads and gradient-based meta-learning, allowing it to learn how to learn better embeddings. Experimental results on three real-world datasets show that Meta-Embedding significantly improves both cold-start and warm-up performances for six existing CTR prediction models.

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Description

Click-through rate (CTR) prediction is crucial in online advertising, and embedding techniques have improved CTR prediction accuracies.

Technical Information

  • File Format: PDF
  • File Size: 854 KB
  • Pages: 10
  • Language: EN
  • Author: Feiyang Pan, Shuokai Li, Xiang Ao, Pingzhong Tang, and Qing He
  • Total Downloads: 113
  • Last Updated: 3 hours ago

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This PDF document about Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings provides comprehensive information and guidance. Whether you're a beginner or advanced user, this resource offers valuable insights into Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings.

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Warm guideWarm tutorialCold-start guideCold-start tutorialAdvertisements: guideAdvertisements: tutorialImproving guideImproving tutorialPredictions guidePredictions tutorialLearning guideLearning tutorialLearn guideLearn tutorialEmbeddings guideEmbeddings tutorial

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