Constructing social intentional corpora to predict click-through rate for search advertising

Yi Ting Chen, Hung Yu Kao

研究成果: Conference contribution

4 引文 斯高帕斯(Scopus)

摘要

In the beginning, search engines provide placements next to the original search results for advertisers on specific keywords. Since users often search for their interests or purchasing decision, timely presenting proper advertisements to users will encourage them to click on search ads. With the rapid growth of advertising, there is a bidding mechanism that advertisers need to bid keywords on their ads. They should carefully compose keywords in order to enhance the opportunity for their ads to be clicked. 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.

原文English
主出版物標題Proceedings of the 25th Conference on Computational Linguistics and Speech Processing, ROCLING 2013
發行者The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)
頁面278-287
頁數10
ISBN(電子)9789573079262
出版狀態Published - 2013 10月 1
事件25th Conference on Computational Linguistics and Speech Processing, ROCLING 2013 - Kaohsiung, Taiwan
持續時間: 2013 10月 42013 10月 5

出版系列

名字Proceedings of the 25th Conference on Computational Linguistics and Speech Processing, ROCLING 2013

Conference

Conference25th Conference on Computational Linguistics and Speech Processing, ROCLING 2013
國家/地區Taiwan
城市Kaohsiung
期間13-10-0413-10-05

All Science Journal Classification (ASJC) codes

  • 言語和聽力
  • 語言與語言學

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