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- W2375775047 abstract "This paper first introduced the basic model of the search engine advertising system(SEAS),which pointed out user clicks were the source of revenue and reveals the importance to predict user behavior.After feature extraction and feature value smoothing on advertising 4-elements,resolved the advertising records into training data and divided then the training data into training set and testing set.It used training set to train the model by SVM and classify the test set to predict user behavior.The evaluation results based on real data show that the model can be used to predict the user behavior in SEAS.Finally,it proves that the most influential feature of user behavior prediction is the click-thought rate and the classification accuracy rate can reach 83.17% in the case of using all features of the model." @default.
- W2375775047 created "2016-06-24" @default.
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- W2375775047 date "2013-01-01" @default.
- W2375775047 modified "2023-09-25" @default.
- W2375775047 title "User behavior classification and feature analysis in search advertising" @default.
- W2375775047 hasPublicationYear "2013" @default.
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