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. Moreover, littlework considers the changing trend of the Interest . In this pa-per, we propose a novel model, named deep Interest Evolu-tion Network (DIEN), for CTR prediction.}
ever, most interest models including DIN regard the behav-ior as the interest directly, while latent interest is hard to be fully reflected by explicit behavior. Previous methods ne-glect to dig the true user interest behind behavior. Moreover, user interest keeps evolving, capturing the dynamic of inter-est is important for interest ...
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