Contextual advertising by combining relevance with click feedback

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 consi...

• Type: Conference
• Year: 2008
• Where: WWW
• Authors: Deepayan Chakrabarti, Deepak Agarwal, Vanja Josifovski
• Keywords: Cosine Similarity Matching, Internet Technology, Logistic Regression Model, Scale Real-world Ad, WWW 2008