Alibaba Innovative Research (AIR) > Machine Learning (algorithm)
Few-shot Learning on CTR/CVR Estimation in Online Advertising

Theme

Machine Learning (algorithm)

Topic

Few-shot Learning for CTR/CVR Estimation in Online Advertising

Background

Click-through rate (CTR) prediction and conversion rate (CVR) prediction are critical tasks in online advertising system. Plenty of businesses depend on CTR/CVR estimation, such as optimized cost-per-click (oCPC) bid strategy, pacing control and optimization of campaigns, smart ad creative selection, gross merchandise volume (GMV) optimization, etc. Thus the performance of CTR/CVR prediction has great impact on the revenue and user experience, and benefits the advertising system for its performance promotion. 

Despite of the huge attention on CTR/CVR prediction from both academia and industry communities, there exists several task-specific problems that make CTR/CVR estimation challenging to handle. For instance,

  • Lack of labeled samples in specific scenarios: E-commerce marketing campaigns and advertising materials are changing frequently. Although huge samples can be collected form online systems, there are only a few labeled data of specific marketing scenarios or new ads. And in the task of conversion rate prediction, there exist problems of delayed feedback and sparse positive samples;
  • Cold-start problem: In Taobao online advertising system, there are a large number of new advertisements every day, especially for Alibaba's Double Eleven Sale (11/11). It is challenging to estimate CTR/CVR of these new ads;
  • Costly hyperparameter tuning: It is costly to tune proper hyperparameters of CTR/CVR models for different similar scenarios. 

Zero/ Few-shot learning refers to the technique of training model with zero or a small amount data, contrary to the normal practice where a large amount of data is used. There are a lot of works about few-shot learning in the field of computer vision, but not for CTR/CVR prediction. We hope to mitigate the aforementioned challenges by utilizing techniques like Zero/ Few-shot learning.

Target

  • Achieve better performance than state-of-the-art in CTR/CVR estimation of specific marketing scenarios or new ads
  • Model/ Framework that can be applied to different tasks effectively
  • Impactful research paper publication

Related Research Topics

  • Zero/One/Few shot learning / Meta-learning for deep networks
  • Deep learning for search engines, recommendations and online advertising
  • Sample selection bias correction theory / Counterfactual evaluation and learning
  • Multi-task models for CTR/CVR estimation

 

Suggested Collaboration Method

AIR (Alibaba Innovative Research), one-year collaboration project. 

 

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