Multi-Epoch learning with Data Augmentation for Deep Click-Through Rate Prediction
- URL: http://arxiv.org/abs/2407.01607v1
- Date: Thu, 27 Jun 2024 04:00:15 GMT
- Title: Multi-Epoch learning with Data Augmentation for Deep Click-Through Rate Prediction
- Authors: Zhongxiang Fan, Zhaocheng Liu, Jian Liang, Dongying Kong, Han Li, Peng Jiang, Shuang Li, Kun Gai,
- Abstract summary: We introduce a novel Multi-Epoch learning with Data Augmentation (MEDA) framework, suitable for both non-continual and continual learning scenarios.
MEDA minimizes overfitting by reducing the dependency of the embedding layer on subsequent training data.
Our findings confirm that pre-trained layers can adapt to new embedding spaces, enhancing performance without overfitting.
- Score: 53.88231294380083
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper investigates the one-epoch overfitting phenomenon in Click-Through Rate (CTR) models, where performance notably declines at the start of the second epoch. Despite extensive research, the efficacy of multi-epoch training over the conventional one-epoch approach remains unclear. We identify the overfitting of the embedding layer, caused by high-dimensional data sparsity, as the primary issue. To address this, we introduce a novel and simple Multi-Epoch learning with Data Augmentation (MEDA) framework, suitable for both non-continual and continual learning scenarios, which can be seamlessly integrated into existing deep CTR models and may have potential applications to handle the "forgetting or overfitting" dilemma in the retraining and the well-known catastrophic forgetting problems. MEDA minimizes overfitting by reducing the dependency of the embedding layer on subsequent training data or the Multi-Layer Perceptron (MLP) layers, and achieves data augmentation through training the MLP with varied embedding spaces. Our findings confirm that pre-trained MLP layers can adapt to new embedding spaces, enhancing performance without overfitting. This adaptability underscores the MLP layers' role in learning a matching function focused on the relative relationships among embeddings rather than their absolute positions. To our knowledge, MEDA represents the first multi-epoch training strategy tailored for deep CTR prediction models. We conduct extensive experiments on several public and business datasets, and the effectiveness of data augmentation and superiority over conventional single-epoch training are fully demonstrated. Besides, MEDA has exhibited significant benefits in a real-world online advertising system.
Related papers
- Beyond Efficiency: Molecular Data Pruning for Enhanced Generalization [30.738229850748137]
MolPeg is a Molecular data Pruning framework for enhanced Generalization.
It focuses on the source-free data pruning scenario, where data pruning is applied with pretrained models.
It consistently outperforms existing DP methods across four downstream tasks.
arXiv Detail & Related papers (2024-09-02T09:06:04Z) - Denoising Pre-Training and Customized Prompt Learning for Efficient Multi-Behavior Sequential Recommendation [69.60321475454843]
We propose DPCPL, the first pre-training and prompt-tuning paradigm tailored for Multi-Behavior Sequential Recommendation.
In the pre-training stage, we propose a novel Efficient Behavior Miner (EBM) to filter out the noise at multiple time scales.
Subsequently, we propose to tune the pre-trained model in a highly efficient manner with the proposed Customized Prompt Learning (CPL) module.
arXiv Detail & Related papers (2024-08-21T06:48:38Z) - Continual Learning with Pre-Trained Models: A Survey [61.97613090666247]
Continual Learning aims to overcome the catastrophic forgetting of former knowledge when learning new ones.
This paper presents a comprehensive survey of the latest advancements in PTM-based CL.
arXiv Detail & Related papers (2024-01-29T18:27:52Z) - PREM: A Simple Yet Effective Approach for Node-Level Graph Anomaly
Detection [65.24854366973794]
Node-level graph anomaly detection (GAD) plays a critical role in identifying anomalous nodes from graph-structured data in domains such as medicine, social networks, and e-commerce.
We introduce a simple method termed PREprocessing and Matching (PREM for short) to improve the efficiency of GAD.
Our approach streamlines GAD, reducing time and memory consumption while maintaining powerful anomaly detection capabilities.
arXiv Detail & Related papers (2023-10-18T02:59:57Z) - MAP: A Model-agnostic Pretraining Framework for Click-through Rate
Prediction [39.48740397029264]
We propose a Model-agnostic pretraining (MAP) framework that applies feature corruption and recovery on multi-field categorical data.
We derive two practical algorithms: masked feature prediction (RFD) and replaced feature detection (RFD)
arXiv Detail & Related papers (2023-08-03T12:55:55Z) - Multi-Epoch Learning for Deep Click-Through Rate Prediction Models [32.80864867251999]
The one-epoch overfitting phenomenon has been widely observed in industrial Click-Through Rate (CTR) applications.
We propose a novel Multi-Epoch learning with Data Augmentation (MEDA), which can be directly applied to most deep CTR models.
arXiv Detail & Related papers (2023-05-31T03:36:50Z) - To Repeat or Not To Repeat: Insights from Scaling LLM under Token-Crisis [50.31589712761807]
Large language models (LLMs) are notoriously token-hungry during pre-training, and high-quality text data on the web is approaching its scaling limit for LLMs.
We investigate the consequences of repeating pre-training data, revealing that the model is susceptible to overfitting.
Second, we examine the key factors contributing to multi-epoch degradation, finding that significant factors include dataset size, model parameters, and training objectives.
arXiv Detail & Related papers (2023-05-22T17:02:15Z) - TWINS: A Fine-Tuning Framework for Improved Transferability of
Adversarial Robustness and Generalization [89.54947228958494]
This paper focuses on the fine-tuning of an adversarially pre-trained model in various classification tasks.
We propose a novel statistics-based approach, Two-WIng NormliSation (TWINS) fine-tuning framework.
TWINS is shown to be effective on a wide range of image classification datasets in terms of both generalization and robustness.
arXiv Detail & Related papers (2023-03-20T14:12:55Z) - Siloed Federated Learning for Multi-Centric Histopathology Datasets [0.17842332554022694]
This paper proposes a novel federated learning approach for deep learning architectures in the medical domain.
Local-statistic batch normalization (BN) layers are introduced, resulting in collaboratively-trained, yet center-specific models.
We benchmark the proposed method on the classification of tumorous histopathology image patches extracted from the Camelyon16 and Camelyon17 datasets.
arXiv Detail & Related papers (2020-08-17T15:49:30Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.