MAP: A Model-agnostic Pretraining Framework for Click-through Rate Prediction

• URL: http://arxiv.org/abs/2308.01737v1
• Date: Thu, 3 Aug 2023 12:55:55 GMT
• Title: MAP: A Model-agnostic Pretraining Framework for Click-through Rate Prediction
• Authors: Jianghao Lin, Yanru Qu, Wei Guo, Xinyi Dai, Ruiming Tang, Yong Yu, Weinan Zhang
• Abstract summary: 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)
• Score: 39.48740397029264
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the widespread application of personalized online services, click-through rate (CTR) prediction has received more and more attention and research. The most prominent features of CTR prediction are its multi-field categorical data format, and vast and daily-growing data volume. The large capacity of neural models helps digest such massive amounts of data under the supervised learning paradigm, yet they fail to utilize the substantial data to its full potential, since the 1-bit click signal is not sufficient to guide the model to learn capable representations of features and instances. The self-supervised learning paradigm provides a more promising pretrain-finetune solution to better exploit the large amount of user click logs, and learn more generalized and effective representations. However, self-supervised learning for CTR prediction is still an open question, since current works on this line are only preliminary and rudimentary. To this end, we propose a Model-agnostic pretraining (MAP) framework that applies feature corruption and recovery on multi-field categorical data, and more specifically, we derive two practical algorithms: masked feature prediction (MFP) and replaced feature detection (RFD). MFP digs into feature interactions within each instance through masking and predicting a small portion of input features, and introduces noise contrastive estimation (NCE) to handle large feature spaces. RFD further turns MFP into a binary classification mode through replacing and detecting changes in input features, making it even simpler and more effective for CTR pretraining. Our extensive experiments on two real-world large-scale datasets (i.e., Avazu, Criteo) demonstrate the advantages of these two methods on several strong backbones (e.g., DCNv2, DeepFM), and achieve new state-of-the-art performance in terms of both effectiveness and efficiency for CTR prediction.

Related papers

• An accuracy improving method for advertising click through rate prediction based on enhanced xDeepFM model [0.0]
  This paper proposes an improved CTR prediction model based on the xDeepFM architecture. By integrating a multi-head attention mechanism, the model can simultaneously focus on different aspects of feature interactions. Experimental results on the Criteo dataset demonstrate that the proposed model outperforms other state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-11-21T03:21:29Z)
• NeSHFS: Neighborhood Search with Heuristic-based Feature Selection for Click-Through Rate Prediction [1.3805049652130312]
  Click-through-rate (CTR) prediction plays an important role in online advertising and ad recommender systems. We propose a CTR algorithm named Neighborhood Search with Heuristic-based Feature Selection (NeSHFS) to enhance CTR prediction performance.
  arXiv  Detail & Related papers  (2024-09-13T10:43:18Z)
• Multi-Epoch learning with Data Augmentation for Deep Click-Through Rate Prediction [53.88231294380083]
  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.
  arXiv  Detail & Related papers  (2024-06-27T04:00:15Z)
• TF4CTR: Twin Focus Framework for CTR Prediction via Adaptive Sample Differentiation [14.047096669510369]
  This paper introduces a novel CTR prediction framework by integrating the plug-and-play Twin Focus (TF) Loss, Sample Selection Embedding Module (SSEM), and Dynamic Fusion Module (DFM) Experiments on five real-world datasets confirm the effectiveness and compatibility of the framework.
  arXiv  Detail & Related papers  (2024-05-06T05:22:40Z)
• Tuning Pre-trained Model via Moment Probing [62.445281364055795]
  We propose a novel Moment Probing (MP) method to explore the potential of LP. MP performs a linear classification head based on the mean of final features. Our MP significantly outperforms LP and is competitive with counterparts at less training cost.
  arXiv  Detail & Related papers  (2023-07-21T04:15:02Z)
• Meta-Wrapper: Differentiable Wrapping Operator for User Interest Selection in CTR Prediction [97.99938802797377]
  Click-through rate (CTR) prediction, whose goal is to predict the probability of the user to click on an item, has become increasingly significant in recommender systems. Recent deep learning models with the ability to automatically extract the user interest from his/her behaviors have achieved great success. We propose a novel approach under the framework of the wrapper method, which is named Meta-Wrapper.
  arXiv  Detail & Related papers  (2022-06-28T03:28:15Z)
• Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach [80.8446673089281]
  We propose a new learning paradigm with graph representation and learning. Our framework contains two modules: 1) a backbone network (e.g., feedforward neural nets) as a lower model takes features as input and outputs predicted labels; 2) a graph neural network as an upper model learns to extrapolate embeddings for new features via message passing over a feature-data graph built from observed data.
  arXiv  Detail & Related papers  (2021-10-09T09:02:45Z)
• Calibrating Class Activation Maps for Long-Tailed Visual Recognition [60.77124328049557]
  We present two effective modifications of CNNs to improve network learning from long-tailed distribution. First, we present a Class Activation Map (CAMC) module to improve the learning and prediction of network classifiers. Second, we investigate the use of normalized classifiers for representation learning in long-tailed problems.
  arXiv  Detail & Related papers  (2021-08-29T05:45:03Z)
• Iterative Boosting Deep Neural Networks for Predicting Click-Through Rate [15.90144113403866]
  The click-through rate (CTR) reflects the ratio of clicks on a specific item to its total number of views. XdBoost is an iterative three-stage neural network model influenced by the traditional machine learning boosting mechanism.
  arXiv  Detail & Related papers  (2020-07-26T09:41:16Z)

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.