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Representative Negative Instance Generation for Online Ad Targeting

Published: 19 October 2020 Publication History

Abstract

Online ad targeting can be formulated as a problem of learning the relevance ranking among possible audiences for a given ad. It has to deal with the massive number of negative,i.e., non-interacted, instances in impression data due to the nature of this service, and thus suffers from data imbalance problem. In this work, we tackle this problem by improving the quality of negative instances used in training the targeting model. We propose to enhance the generalization capability by introducing unobserved data as possible negative instances, and extract more reliable negative instances from the observed negatives in impression data. However, this idea is non-trivial to implement because of the limited learning signal and existing noise signal. To this end, we design a novel RNIG method (short for Representative Negative Instance Generator) to leverage feature matching technique. It aims to generate reliable negative instances that are similar to the observed negatives and further improves the representativeness of generated negatives by matching the most important feature. Extensive experiments on the real-world ad targeting dataset show that our RNIG model has achieved a relative improvement of more than 5%.

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Online ad targeting can be formulated as a problem of learning the relevance ranking among possible audiences for a given ad. It has to deal with the massive number of negative instances in impression data and thus suffers from data imbalance problem. In this work, we tackle this problem by improving the quality of negative instances used in training the targeting model. We propose to enhance the generalization capability by introducing unobserved data as possible negative instances, and extract more reliable negative instances from the observed negatives in impression data. However, this idea is non-trivial to implement because of the limited learning signal and existing noise signal. To this end, we design a novel RNIG method (short for Representative Negative Instance Generator) to leverage feature matching technique. It aims to generate reliable negative instances that are similar to the observed negatives and further improves the representativeness of generated negatives.

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cover image ACM Conferences
CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management
October 2020
3619 pages
ISBN:9781450368599
DOI:10.1145/3340531
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Published: 19 October 2020

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Author Tags

  1. ad targeting
  2. adversarial learning
  3. feature matching
  4. negative sampling

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  • Short-paper

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  • The National Key Research and Development Program of China
  • Beijing National Research Center for Information Science and Technology
  • Beijing Natural Science Foundation

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CIKM '20
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