Up or down? Click-through rate prediction from social intention for search advertising

Yi Ting Chen, Hung-Yu Kao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In search advertising, advertisers should carefully compose keywords in order to enhance the opportunity for ads to be clicked. Thus, timely presenting proper advertisements to users will encourage them to click on search ads. Until now, how to efficiently improve the ad performance to earn more clicks remains a main task. In this paper, we focus on the scope of smart phone and produce a social intentional model with advertising based features to forecast future trend on ads' click-through rate (CTR). In terms of social intentional model, we analyze Chinese text content of technology forum to derive social intentional factors which are Hotness, Sentiment, Promotion, and Event. Our results indicate that with knowing public opinions or occurring events beforehand can efficiently enhance click prediction. This will be very helpful for advertisers on adjusting bidding keywords to improve ad performance via social intention.

Original languageEnglish
Title of host publicationProceedings - 15th International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2013
Pages102-106
Number of pages5
DOIs
Publication statusPublished - 2013 Dec 1
Event15th International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2013 - Vienna, Austria
Duration: 2013 Dec 22013 Dec 4

Publication series

NameACM International Conference Proceeding Series

Other

Other15th International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2013
CountryAustria
CityVienna
Period13-12-0213-12-04

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

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