AdaSparse: Learning Adaptively Sparse Structures for Multi-Domain Click-Through Rate Prediction
Click-through rate (CTR) prediction is a fundamental technique in recommendation and advertising systems. Recent studies have proved that learning a unified model to serve multiple domains is effective to improve the overall performance. However, it is still challenging to improve generalization across domains under limited training data, and hard to deploy current solutions due to their computational complexity. In this paper, we propose a simple yet effective framework AdaSparse for multi-domain CTR prediction, which learns adaptively sparse structure for each domain, achieving better generalization across domains with lower computational cost. In AdaSparse, we introduce domain-aware neuron-level weighting factors to measure the importance of neurons, with that for each domain our model can prune redundant neurons to improve generalization. We further add flexible sparsity regularizations to control the sparsity ratio of learned structures. Offline and online experiments show that AdaSparse outperforms previous multi-domain CTR models significantly.
Code (0)
등록된 구현이 없습니다.
Tasks
Click-Through Rate PredictionSimilar Papers 제목 키워드 기반
Multi-Domain Graph Foundation Models: Robust Knowledge Transfer via Topology Alignment
Recent advances in CV and NLP have inspired researchers to develop general-purpose graph foundation models through pre-training across diverse domains. However, a fundamental challenge arises from the substantial differe…
Domain GeneralizationTransfer LearningVideo Matting via Sparse and Low-Rank Representation
We introduce a novel method of video matting via sparse and low-rank representation. Previous matting methods [10, 9] introduced a nonlocal prior to estimate the alpha matte and have achieved impressive results on some d…
Image MattingVideo MattingLearning Subspace-Preserving Sparse Attention Graphs from Heterogeneous Multiview Data
The high-dimensional features extracted from large-scale unlabeled data via various pretrained models with diverse architectures are referred to as heterogeneous multiview data. Most existing unsupervised transfer learni…
Representation LearningTransfer LearningGraph LearningScaffold Diffusion: Sparse Multi-Category Voxel Structure Generation with Discrete Diffusion
Generating realistic sparse multi-category 3D voxel structures is difficult due to the cubic memory scaling of voxel structures and moreover the significant class imbalance caused by sparsity. We introduce Scaffold Diffu…
Time-Series Domain Adaptation via Sparse Associative Structure Alignment: Learning Invariance and Variance
Domain adaptation on time-series data is often encountered in the industry but received limited attention in academia. Most of the existing domain adaptation methods for time-series data borrow the ideas from the existin…
Domain AdaptationTime SeriesTime Series AnalysisTransfer Learning+1