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Refereed Papers
Track: Search: Ranking and Retrieval Enhancement
Paper Title:
Contextual Advertising by Combining Relevance with Click Feedback
Authors:
- Deepayan Chakrabarti(Yahoo! Research)
- Deepak Agarwal(Yahoo! Research)
- Vanja Josifovski(Yahoo! Research)
Abstract:
Contextual advertising supports much of the Web's ecosystem today.
User experience and revenue (shared by the site publisher ad the ad
network) depend on the relevance of the displayed ads to the
page content. As with other document retrieval systems, relevance is
provided by scoring the match between individual ads (documents)
and the content of the page where the ads are shown (query). In this
paper we show how this match can be improved significantly by
augmenting the ad-page scoring function with extra parameters from a
logistic regression model on the words in the pages and ads. A key
property of the proposed model is that it can be mapped to standard
cosine similarity matching and is suitable for efficient and scalable
implementation over inverted indexes. The model parameter values are
learnt from logs containing ad impressions and clicks, with shrinkage
estimators being used to combat sparsity. To scale our computations to
train on an extremely large training corpus consisting of several
gigabytes of data, we parallelize our fitting algorithm in a Hadoop framework. Experimental evaluation is provided showing improved click
prediction over a holdout set of impression and click events from a
large scale real-world ad placement engine. Our best model achieves a
25% lift in precision relative to a traditional information
retrieval model which is based on cosine similarity, for recalling
10% of the clicks in our test data.
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