Transcription of Deep Interest Evolution Network for Click-Through Rate ...
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Deep Interest Evolution Network for Click-Through Rate ...
deep Interest Evolution Network for Click-Through Rate PredictionGuorui Zhou*, Na Mou , Ying Fan, Qi Pi, Weijie Bian,Chang Zhou, Xiaoqiang ZhuandKun GaiAlibaba Inc, Beijing, China{ , , , , , , , rate (CTR) prediction, whose goal is to esti-mate the probability of a user clicking on the item, has be-come one of the core tasks in the advertising system. ForCTR prediction model, it is necessary to capture the latentuser Interest behind the user behavior data. Besides, consid-ering the changing of the external environment and the in-ternal cognition, user Interest evolves over time are several CTR prediction methods for Interest mod-eling, while most of them regard the representation of behav-ior as the Interest directly, and lack specially modeling forlatent Interest behind the concrete behavior.}
Alibaba Inc, Beijing, China fguorui.xgr, mouna.mn, fanying.fy, piqi.pq, weijie.bwj, ericzhou.zc, xiaoqiang.zxq, jingshi.gkg@alibaba-inc.com Abstract Click-through rate (CTR) prediction, whose goal is to esti-mate the probability of a user clicking on the item, has be-come one of the core tasks in the advertising system. For
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