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Implicit User Awareness Modeling via Candidate Items for CTR Prediction in Search Ads by Kaifu Zheng; Lu Wang; Yu Li; Xusong Chen; Hu Liu; Jing Lu; Xiwei Zhao; Changping Peng; Zhangang Lin; Jingping Shao is a scholarly article available to read on EtoBox.

What is Implicit User Awareness Modeling via Candidate Items for CTR Prediction in Search Ads about?

Click-through rate (CTR) prediction plays a crucial role in sponsored search advertising (search ads). User click behavior usually showcases strong comparison patterns among relevant/competing items within the user awareness. Explicit user awareness could be characterized by user behavior sequence modeling, which however suffers from issues such as cold start, behavior noise and hidden channels. Instead, in this paper, we study the problem of modeling implicit user awareness about relevant/competing items. We notice that candidate items of the CTR prediction model could play as surrogates for relevant/competing items within the user awareness. Motivated by this finding, we propose a novel framework, named CIM (Candidate Item Modeling), to characterize users' awareness on candidate items. CIM introduces an additional module to encode candidate items into a context vector and therefore is plug-and-play for existing neural network-based CTR prediction models. Offline experiments on a ten-billion-scale production dataset collected from the real traffic of a search advertising system, together with the corresponding online A/B testing, demonstrate CIM's superior performance. Notably, CI

Author

Kaifu Zheng; Lu Wang; Yu Li; Xusong Chen; Hu Liu; Jing Lu; Xiwei Zhao; Changping Peng; Zhangang Lin; Jingping Shao

Publisher

ACM

Published

2022

Language

EN