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.}
arXiv:1809.03672v5 [stat.ML] 16 Nov 2018. tive interests’ influence on interest evolution, while weakens irrelative interests’ effect that results from interest drifting. With the introduction of attentional mechanism into update gate, AUGRU can lead to the specific interest evolving pro-
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