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This paper introduces FLMNet, a novel approach to Click-Through Rate (CTR) prediction that eliminates the need for random sampling during self-distillation by deriving a closed-form penalty for network disagreement. By leveraging dropout patterns and seed variations as implicit seeds, the method achieves top-tier accuracy on five benchmarks while maintaining single-network inference efficiency. The work provides theoretical insight into why ensemble-like gains can be recovered without explicit sampling, advancing the state of deep learning in recommender systems.
FLMNet: Self-Ensemble Distillation from Masks to Seeds for Single-Tower CTR Prediction
arXiv:2610.26143v1 Announce Type: cross Abstract: Click-through rate (CTR) prediction underpins recommender systems and online advertising, where high-dimensional categorical features interact in combinatorially many ways. Several recent CTR models, such as FinalNet and QNN, report higher accuracy when a network also learns from its own averaged predictions, for example by running the same network under different dropout patterns and training each run toward the average. Why this self-distillation helps, and whether its random sampling is needed, remains unclear. This work shows that as the number of dropout patterns grows without bound, the objective reduces exactly to the label loss of the logit-averaged prediction plus a penalty on how much the dropout copies of the network disagree. When dropout does not affect how a network gates its features, this penalty can be approximated in closed form, and FLMNet is designed on this principle and trains without sampling. Networks trained from different seeds disagree in the same form, and distilling several of them into one network recovers part of their ensemble gain. On five benchmarks against seventeen baselines, the resulting models take the top two places in accuracy with a single network at inference, and the gains of the penalty and of distillation, where measurable, largely add up.
Abstract:Click-through rate (CTR) prediction underpins recommender systems and online advertising, where high-dimensional categorical features interact in combinatorially many ways. Several recent CTR models, such as FinalNet and QNN, report higher accuracy when a network also learns from its own averaged predictions, for example by running the same network under different dropout patterns and training each run toward the average. Why this self-distillation helps, and whether its random sampling is needed, remains unclear. This work shows that as the number of dropout patterns grows without bound, the objective reduces exactly to the label loss of the logit-averaged prediction plus a penalty on how much the dropout copies of the network disagree. When dropout does not affect how a network gates its features, this penalty can be approximated in closed form, and FLMNet is designed on this principle and trains without sampling. Networks trained from different seeds disagree in the same form, and distilling several of them into one network recovers part of their ensemble gain. On five benchmarks against seventeen baselines, the resulting models take the top two places in accuracy with a single network at inference, and the gains of the penalty and of distillation, where measurable, largely add up.
44 pages, 10 figures, 15 tables. Code: this https URL
Information Retrieval (cs.IR); Machine Learning (cs.LG)
arXiv:2610.26143 [cs.IR]
(or arXiv:2610.26143v1 [cs.IR] for this version)
https://doi.org/10.48550/arXiv.2610.26143 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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