Looking at CTR Prediction Again: Is Attention All You Need?

• URL: http://arxiv.org/abs/2105.05563v1
• Date: Wed, 12 May 2021 10:27:14 GMT
• Title: Looking at CTR Prediction Again: Is Attention All You Need?
• Authors: Yuan Cheng and Yanbo Xue
• Abstract summary: Click-through rate (CTR) prediction is a critical problem in web search, recommendation systems and online advertisement displaying. We use the discrete choice model in economics to redefine the CTR prediction problem, and propose a general neural network framework built on self-attention mechanism. It is found that most existing CTR prediction models align with our proposed general framework.
• Score: 4.873362301533825
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Click-through rate (CTR) prediction is a critical problem in web search, recommendation systems and online advertisement displaying. Learning good feature interactions is essential to reflect user's preferences to items. Many CTR prediction models based on deep learning have been proposed, but researchers usually only pay attention to whether state-of-the-art performance is achieved, and ignore whether the entire framework is reasonable. In this work, we use the discrete choice model in economics to redefine the CTR prediction problem, and propose a general neural network framework built on self-attention mechanism. It is found that most existing CTR prediction models align with our proposed general framework. We also examine the expressive power and model complexity of our proposed framework, along with potential extensions to some existing models. And finally we demonstrate and verify our insights through some experimental results on public datasets.

Related papers

• 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)
• MRCLens: an MRC Dataset Bias Detection Toolkit [82.44296974850639]
  We introduce MRCLens, a toolkit that detects whether biases exist before users train the full model. For the convenience of introducing the toolkit, we also provide a categorization of common biases in MRC.
  arXiv  Detail & Related papers  (2022-07-18T21:05:39Z)
• 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)
• Rethinking Position Bias Modeling with Knowledge Distillation for CTR Prediction [8.414183573280779]
  This work proposes a knowledge distillation framework to alleviate the impact of position bias and leverage position information to improve CTR prediction. The proposed method has been deployed in the real world online ads systems, serving main traffic on one of the world's largest e-commercial platforms.
  arXiv  Detail & Related papers  (2022-04-01T07:58:38Z)
• Click-Through Rate Prediction in Online Advertising: A Literature Review [0.0]
  We make a systematic literature review on state-of-the-art and latest CTR prediction research. We give a classification of state-of-the-art CTR prediction models in the extant literature. We identify current research trends, main challenges and potential future directions worthy of further explorations.
  arXiv  Detail & Related papers  (2022-02-22T01:05:38Z)
• Click-through Rate Prediction with Auto-Quantized Contrastive Learning [46.585376453464114]
  We consider whether the user behaviors are rich enough to capture the interests for prediction, and propose an Auto-Quantized Contrastive Learning (AQCL) loss to regularize the model. The proposed framework is agnostic to different model architectures and can be trained in an end-to-end fashion.
  arXiv  Detail & Related papers  (2021-09-27T04:39:43Z)
• XCrossNet: Feature Structure-Oriented Learning for Click-Through Rate Prediction [46.72935114485706]
  We propose a novel Extreme Cross Network, abbreviated XCrossNet, which aims at learning dense and sparse feature interactions in an explicit manner. XCrossNet as a feature structure-oriented model leads to a more expressive representation and a more precise CTR prediction. Experimental studies on Criteo Kaggle dataset show significant improvement of XCrossNet over state-of-the-art models on both effectiveness and efficiency.
  arXiv  Detail & Related papers  (2021-04-22T07:37:36Z)
• Efficient Click-Through Rate Prediction for Developing Countries via Tabular Learning [2.916402752324148]
  Click-Through Rate (CTR) prediction models are difficult to be deployed due to the limited computing resources. In this paper, we show that tabular learning models are more efficient and effective in CTR prediction.
  arXiv  Detail & Related papers  (2021-04-15T16:07:25Z)
• BARS-CTR: Open Benchmarking for Click-Through Rate Prediction [30.000261789268063]
  Click-through rate (CTR) prediction is a critical task for many applications. In recent years, CTR prediction has been widely studied in both academia and industry. There is still a lack of standardized benchmarks and uniform evaluation protocols for CTR prediction research.
  arXiv  Detail & Related papers  (2020-09-12T13:34:22Z)
• Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction [64.03526633651218]
  Click-Through Rate (CTR) prediction is one of the most important machine learning tasks in recommender systems. We propose an automated interaction architecture discovering framework for CTR prediction named AutoCTR.
  arXiv  Detail & Related papers  (2020-06-29T04:33:01Z)
• End-to-End Object Detection with Transformers [88.06357745922716]
  We present a new method that views object detection as a direct set prediction problem. Our approach streamlines the detection pipeline, effectively removing the need for many hand-designed components. The main ingredients of the new framework, called DEtection TRansformer or DETR, are a set-based global loss.
  arXiv  Detail & Related papers  (2020-05-26T17:06:38Z)

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.