Miao Lu

Campaign Representation Learning in Advertisement

I'll present our work on training deep learning models in conversion rate (CVR) prediction in online advertisement. Specifically, I would like to describe an attention joint embedding model to simultaneously learn heterogeneous sources of information for ads campaigns. The campaign representations were able to capture similarities on ad contents, conversion rules and targeting user groups. Extensive experiments on real-world dataset were conducted to show the effectiveness of the proposed attention campaign embedding model, in comparison with different baselines in CVR prediction.

Key Takeaways:

  • Representation learning with heterogeneous information
  • Conversion rate prediction
  • Cold-start Campaign

Miao is a research scientist from Yahoo Research, working on Native/Display/Search Ads Recommendation and Forecasting, leading the corporate traffic and revenue forecasting projects. He has strong interdisciplinary background in Statistics, Machine Learning and Data Mining, with wide applications in biomedical science and internet technology. Before joining Yahoo, he obtained a PhD / MS in statistics from University of Virginia, and a BS in statistics from Zhejiang University.

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