Abstract
Contextual advertising is an important part of the Web economy today. Profit is linked to the interest that users find in the ads presented to them. The problem is for contextual advertising platforms to select the most relevant ads. Simple keyword matching techniques for matching ads to page content give poor accuracy. Problems such as homonymy, polysemy, limited intersection between content and selection keywords as well as context mismatch can significantly degrade the precision of ads selection. In this paper, we propose a method for improving the relevance of contextual ads based on “Wikipedia matching”. It is a technique that uses Wikipedia articles as “reference points” for ads selection. In our research, we worked on English language, but it is possible to port the algorithm to other languages.
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Pak, A. (2011). Using Wikipedia to Improve Precision of Contextual Advertising. In: Vetulani, Z. (eds) Human Language Technology. Challenges for Computer Science and Linguistics. LTC 2009. Lecture Notes in Computer Science(), vol 6562. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20095-3_49
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DOI: https://doi.org/10.1007/978-3-642-20095-3_49
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