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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.
arXiv:2609.37905v1 Announce Type: cross Abstract: Click-Through Rate prediction, a core task in recommendation and advertising systems, relies on modeling interactions among sparse categorical featur...
arXiv:2606. 10243v1 Announce Type: new Abstract: Offsite conversion rate (OCVR) prediction is an important ranking problem in computational recommendation systems.
arXiv:2607. 22700v1 Announce Type: new Abstract: Post-click conversion rate (PCVR) prediction is central to industrial recommendation, but remains challenged by the structural mismatch between sparse, unordered multi-field features and long, domain-specific behavior histories.
arXiv:2501. 02173v2 Announce Type: replace-cross Abstract: The deployment of Large Language Models (LLMs) in recommender systems for predicting Click-Through Rates (CTR) necessitates a delicate balance between computational efficiency and predictive accuracy.
arXiv:2607. 24804v2 Announce Type: replace-cross Abstract: Recommendation systems have undergone significant transformations in the past years.