https://scholars.lib.ntu.edu.tw/handle/123456789/413137
Title: | Learning to predict the cost-per-click for your ad words | Authors: | Wang C.-J. HSIN-HSI CHEN |
Keywords: | ad ranking;CPC prediction;search engine optimization | Issue Date: | 2012 | Start page/Pages: | 2291-2294 | Source: | ACM International Conference Proceeding Series | Abstract: | In Internet ad campaign, ranking of an ad on search result pages depends on a cost-per-click (CPC) of ad words offered by an advertiser and a quality score estimated by a search engine. Bidding for ad words with a higher CPC is more competitive than bidding for the same ad words with a lower CPC in the ad ranking competition. However, offering a higher CPC will increase a burden on advertisers. In contrast, offering a lower CPC may decrease the exposure rate of their ads. Thus, how to select an appropriate CPC for ad words is indispensable for advertisers. In this paper, we extract different semantic levels of features, such as named entities, topic terminologies, and individual words from a large-scale real-world ad words corpus, and explore various learning based prediction algorithms. The thorough experimental results show that the CPC prediction models considering more ad words semantics achieve better prediction performance, and the prediction model using the support vector regression (SVR) and features from all semantic levels performs the best. ? 2012 ACM. |
Description: | 21st ACM International Conference on Information and Knowledge Management, CIKM 2012, 29 October 2012 through 2 November 2012, Maui, HI |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/413137 | ISBN: | 9781450311564 | DOI: | 10.1145/2396761.2398623 |
Appears in Collections: | 資訊工程學系 |
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