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CIKM
2009
Springer

Translating relevance scores to probabilities for contextual advertising

9 years 5 months ago
Translating relevance scores to probabilities for contextual advertising
Information retrieval systems conventionally assess document relevance using the bag of words model. Consequently, relevance scores of documents retrieved for different queries are often difficult to compare, as they are computed on different (or even disjoint) sets of textual features. Many tasks, such as federation of search results or global thresholding of relevance scores, require that scores be globally comparable. To achieve this aim, we propose methods for non-monotonic transformation of relevance scores into probabilities for a contextual advertising selection engine that uses a vector space model. The calibration of the raw scores is based on historical click data. Categories and Subject Descriptors H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval—retrieval models General Terms Algorithms, Experimentation, Measurement, Performance Keywords Relevance scores, probability of relevance, logistic regression, online advertising
Deepak Agarwal, Evgeniy Gabrilovich, Robert Hall,
Added 26 May 2010
Updated 26 May 2010
Type Conference
Year 2009
Where CIKM
Authors Deepak Agarwal, Evgeniy Gabrilovich, Robert Hall, Vanja Josifovski, Rajiv Khanna
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