Towards better ad experience: Click prediction leveraging sequential networks derived specifically from user search behaviors

Shengzhe Li, Tomoko Izumi, Yu Kuratake, Jiali Yao, Jerry Turner, Daisuke Kawahara, Sadao Kurohashi

研究成果: Paper査読

抄録

We propose a sequential modeling approach to improve click prediction for search engine advertising. Unlike previous studies leveraging advertisement content and their relevance-to-query information, we employ only users' search behavioral features such as users' query texts and actual click records of both organic search results and advertisements. By leveraging long short-term memory (LSTM) networks, we successfully modeled users' sequential search behaviors and fully utilized them in click predictions. Through experiments conducted with large-scale search log data obtained from an actual commercial search engine, we demonstrated that our method combining users' current and previous search behaviors reaches better prediction performance than baseline methods.

本文言語English
ページ461-470
ページ数10
出版ステータスPublished - 2019
外部発表はい
イベント33rd Pacific Asia Conference on Language, Information and Computation, PACLIC 2019 - Hakodate, Japan
継続期間: 2019 9 132019 9 15

Conference

Conference33rd Pacific Asia Conference on Language, Information and Computation, PACLIC 2019
国/地域Japan
CityHakodate
Period19/9/1319/9/15

ASJC Scopus subject areas

  • 言語および言語学
  • コンピュータ サイエンス(その他)

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