ŞEHİR e-arşiv

Rightscope:detecting search campaingns positive and negativequeries

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dc.contributor.author Alchalabi, Alaa Eddin
dc.contributor.author Elsharnoby, Mohammed
dc.contributor.author Khawaldeh, Saed
dc.date.accessioned 2017-10-17T13:37:35Z
dc.date.available 2017-10-17T13:37:35Z
dc.date.issued 2017
dc.identifier.isbn 9781509003907
dc.identifier.uri http://hdl.handle.net/11498/45302
dc.description.abstract In search engine advertising, the right selection of keywords is one of the most important step to maximize the advertisers’ profitability. Keyword suggestion methodologies are used to map the advertisers selection of keywords and the recent popular queries using statistical data. In this project we are trying to classify the searchqueriesintorelevantandnon-relevant. Inotherwords,we are trying to exclude unprofitable queries from the search campaign. As a labeling methodology, we built a Binary Independence Model (BIM) using both statistical and machine learning approaches. The BIM was built by combining scores from three different models: the Relevance Status Value (RSV) of the ProbabilityRankingPrinciple(PRP)model,LogisticRegressionModel’sprobabilities,andaWeightedCostModel(WCM).Scalingthe queries relevance by the estimated cost by the WCM model relativelyscoresthequerieswithlowercostshigherranking. en_US
dc.language.iso eng en_US
dc.publisher IEEE en_US
dc.rights info:eu-repo/semantics/embargoedAccess en_US
dc.subject Machine Learning--Congresses en_US
dc.subject Cybernetics--Congresses en_US
dc.subject Artificial Intelligence--Congresses en_US
dc.subject Makine Öğrenimi--Kongreler en_US
dc.subject Sibernetik--Kongreler en_US
dc.subject Yapay Zeka--Kongreler en_US
dc.title Rightscope:detecting search campaingns positive and negativequeries en_US
dc.type Article en_US
dc.relation.journal Proceedings of the 2016 International Conference on Machine Learning and Cybernetics en_US

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