Dynamic Parameterized Network for CTR Prediction

• URL: http://arxiv.org/abs/2111.04983v1
• Date: Tue, 9 Nov 2021 08:15:03 GMT
• Title: Dynamic Parameterized Network for CTR Prediction
• Authors: Jian Zhu, Congcong Liu, Pei Wang, Xiwei Zhao, Guangpeng Chen, Junsheng Jin, Changping Peng, Zhangang Lin, Jingping Shao
• Abstract summary: We proposed a novel plug-in operation, Dynamic ized Operation (DPO), to learn both explicit and implicit interaction instance-wisely. We showed that the introduction of DPO into DNN modules and Attention modules can respectively benefit two main tasks in click-through rate (CTR) prediction. Our Dynamic ized Networks significantly outperforms state-of-the-art methods in the offline experiments on the public dataset and real-world production dataset.
• Score: 6.749659219776502
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning to capture feature relations effectively and efficiently is essential in click-through rate (CTR) prediction of modern recommendation systems. Most existing CTR prediction methods model such relations either through tedious manually-designed low-order interactions or through inflexible and inefficient high-order interactions, which both require extra DNN modules for implicit interaction modeling. In this paper, we proposed a novel plug-in operation, Dynamic Parameterized Operation (DPO), to learn both explicit and implicit interaction instance-wisely. We showed that the introduction of DPO into DNN modules and Attention modules can respectively benefit two main tasks in CTR prediction, enhancing the adaptiveness of feature-based modeling and improving user behavior modeling with the instance-wise locality. Our Dynamic Parameterized Networks significantly outperforms state-of-the-art methods in the offline experiments on the public dataset and real-world production dataset, together with an online A/B test. Furthermore, the proposed Dynamic Parameterized Networks has been deployed in the ranking system of one of the world's largest e-commerce companies, serving the main traffic of hundreds of millions of active users.

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