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.
|出版ステータス||Published - 2019|
|イベント||33rd Pacific Asia Conference on Language, Information and Computation, PACLIC 2019 - Hakodate, Japan|
継続期間: 2019 9 13 → 2019 9 15
|Conference||33rd Pacific Asia Conference on Language, Information and Computation, PACLIC 2019|
|Period||19/9/13 → 19/9/15|
ASJC Scopus subject areas
- コンピュータ サイエンス（その他）