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arXiv AI By Chaohua Yang, Fuyuan Lyu, Xing Tang, Dugang Liu, Xiuqiang He, Zhong Ming

Towards a Unified Framework of Optimal Input-Side Dimensional Configuration for Deep CTR Prediction

Read the original on arXiv AI →

The paper introduces OptIDC, a unified framework for optimizing input-side dimensional configuration in deep click‑through rate (CTR) prediction models. OptIDC jointly learns sample, feature, and embedding‑unit selections and employs a selection‑weighted Optimal Transport mechanism to coordinate decisions across levels, reducing conflicts while preserving semantics. Experiments on four real‑world datasets show that OptIDC consistently outperforms existing input‑side selection methods in terms of AUC.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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