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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}

Guorui Zhou*, Na Mou ... tention to activate the historical behaviors w.r.t. given target item locally, and captures the diversity characteristic of user interests successfully. However, DIN is weak in capturing the dependencies between sequential behaviors.

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