Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction

10 Jun 2020 Pi Qi Xiaoqiang Zhu Guorui Zhou Yujing Zhang Zhe Wang Lejian Ren Ying Fan Kun Gai

Rich user behavior data has been proven to be of great value for click-through rate prediction tasks, especially in industrial applications such as recommender systems and online advertising. Both industry and academy have paid much attention to this topic and propose different approaches to modeling with long sequential user behavior data... (read more)

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