A Click-Through Rate Prediction Method Based on Cross-Importance of Multi-Order Features

• URL: http://arxiv.org/abs/2405.08852v1
• Date: Tue, 14 May 2024 16:05:57 GMT
• Title: A Click-Through Rate Prediction Method Based on Cross-Importance of Multi-Order Features
• Authors: Hao Wang, Nao Li,
• Abstract summary: This paper proposes a new model, FiiNet (Multiple Order Feature Interaction Importance Neural Networks) The model first uses the selective kernel network (SKNet) to explicitly construct multi-order feature crosses. It dynamically learns the importance of feature interaction combinations in a fine grained manner.
• Score: 4.820576346277399
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most current click-through rate prediction(CTR)models create explicit or implicit high-order feature crosses through Hadamard product or inner product, with little attention to the importance of feature crossing; only few models are either limited to the second-order explicit feature crossing, implicitly to high-order feature crossing, or can learn the importance of high-order explicit feature crossing but fail to provide good interpretability for the model. This paper proposes a new model, FiiNet (Multiple Order Feature Interaction Importance Neural Networks). The model first uses the selective kernel network (SKNet) to explicitly construct multi-order feature crosses. It dynamically learns the importance of feature interaction combinations in a fine grained manner, increasing the attention weight of important feature cross combinations and reducing the weight of featureless crosses. To verify that the FiiNet model can dynamically learn the importance of feature interaction combinations in a fine-grained manner and improve the model's recommendation performance and interpretability, this paper compares it with many click-through rate prediction models on two real datasets, proving that the FiiNet model incorporating the selective kernel network can effectively improve the recommendation effect and provide better interpretability. FiiNet model implementations are available in PyTorch.

Related papers

• Multi-granularity Interest Retrieval and Refinement Network for Long-Term User Behavior Modeling in CTR Prediction [68.90783662117936]
  Click-through Rate (CTR) prediction is crucial for online personalization platforms. Recent advancements have shown that modeling rich user behaviors can significantly improve the performance of CTR prediction. We propose Multi-granularity Interest Retrieval and Refinement Network (MIRRN)
  arXiv  Detail & Related papers  (2024-11-22T15:29:05Z)
• Part-Based Models Improve Adversarial Robustness [57.699029966800644]
  We show that combining human prior knowledge with end-to-end learning can improve the robustness of deep neural networks. Our model combines a part segmentation model with a tiny classifier and is trained end-to-end to simultaneously segment objects into parts. Our experiments indicate that these models also reduce texture bias and yield better robustness against common corruptions and spurious correlations.
  arXiv  Detail & Related papers  (2022-09-15T15:41:47Z)
• X2Parser: Cross-Lingual and Cross-Domain Framework for Task-Oriented Compositional Semantic Parsing [51.81533991497547]
  Task-oriented compositional semantic parsing (TCSP) handles complex nested user queries. We present X2 compared a transferable Cross-lingual and Cross-domain for TCSP. We propose to predict flattened intents and slots representations separately and cast both prediction tasks into sequence labeling problems.
  arXiv  Detail & Related papers  (2021-06-07T16:40:05Z)
• DCAP: Deep Cross Attentional Product Network for User Response Prediction [20.17934000984361]
  We propose a novel architecture Deep Cross Attentional Product Network (DCAP) DCAP keeps cross network's benefits in modeling high-order feature interactions explicitly at the vector-wise level. Our proposed model can be easily implemented and train in parallel.
  arXiv  Detail & Related papers  (2021-05-18T16:27:20Z)
• 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)
• Probabilistic Graph Attention Network with Conditional Kernels for Pixel-Wise Prediction [158.88345945211185]
  We present a novel approach that advances the state of the art on pixel-level prediction in a fundamental aspect, i.e. structured multi-scale features learning and fusion. We propose a probabilistic graph attention network structure based on a novel Attention-Gated Conditional Random Fields (AG-CRFs) model for learning and fusing multi-scale representations in a principled manner.
  arXiv  Detail & Related papers  (2021-01-08T04:14:29Z)
• AdnFM: An Attentive DenseNet based Factorization Machine for CTR Prediction [11.958336595818267]
  We propose a novel model called Attentive DenseNet based Factorization Machines (AdnFM) AdnFM can extract more comprehensive deep features by using all the hidden layers from a feed-forward neural network as implicit high-order features. Experiments on two real-world datasets show that the proposed model can effectively improve the performance of Click-Through-Rate prediction.
  arXiv  Detail & Related papers  (2020-12-20T01:00:39Z)
• Feature Interaction based Neural Network for Click-Through Rate Prediction [5.095988654970358]
  We propose a Feature Interaction based Neural Network (FINN) which is able to model feature interaction via a 3-dimention relation tensor. We show that our deep FINN model outperforms other state-of-the-art deep models such as PNN and DeepFM. It also indicates that our models can effectively learn the feature interactions, and achieve better performances in real-world datasets.
  arXiv  Detail & Related papers  (2020-06-07T03:53:24Z)
• GAMI-Net: An Explainable Neural Network based on Generalized Additive Models with Structured Interactions [5.8010446129208155]
  An explainable neural network based on generalized additive models with structured interactions (GAMI-Net) is proposed to pursue a good balance between prediction accuracy and model interpretability. GAMI-Net is a disentangled feedforward network with multiple additiveworks. Numerical experiments on both synthetic functions and real-world datasets show that the proposed model enjoys superior interpretability.
  arXiv  Detail & Related papers  (2020-03-16T11:51:38Z)
• Model Fusion via Optimal Transport [64.13185244219353]
  We present a layer-wise model fusion algorithm for neural networks. We show that this can successfully yield "one-shot" knowledge transfer between neural networks trained on heterogeneous non-i.i.d. data.
  arXiv  Detail & Related papers  (2019-10-12T22:07:15Z)

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.