Up or Down? Click-Through Rate Prediction from Social Intention for Search Advertising

Authors:
Yi-Ting Chen;Hung-Yu Kao
Affiliations:
Department of Computer Science and Information Engineering, National Cheng Kung University Tainan, Taiwan, R.O.C.;Department of Computer Science and Information Engineering, National Cheng Kung University Tainan, Taiwan, R.O.C.
Venue:
Proceedings of International Conference on Information Integration and Web-based Applications & Services
Year:
2013

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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.