Search Results for author: Junlin Zhang

Found 10 papers, 3 papers with code

ContextNet: A Click-Through Rate Prediction Framework Using Contextual information to Refine Feature Embedding

no code implementations26 Jul 2021 Zhiqiang Wang, Qingyun She, PengTao Zhang, Junlin Zhang

In this paper, We propose a novel CTR Framework named ContextNet that implicitly models high-order feature interactions by dynamically refining each feature's embedding according to the input context.

Click-Through Rate Prediction Recommendation Systems

Leaf-FM: A Learnable Feature Generation Factorization Machine for Click-Through Rate Prediction

no code implementations26 Jul 2021 Qingyun She, Zhiqiang Wang, Junlin Zhang

For example, the continuous features are usually transformed to the power forms by adding a new feature to allow it to easily form non-linear functions of the feature.

Click-Through Rate Prediction Feature Engineering +1

MaskNet: Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask

no code implementations9 Feb 2021 Zhiqiang Wang, Qingyun She, Junlin Zhang

We also turn the feed-forward layer in DNN model into a mixture of addictive and multiplicative feature interactions by proposing MaskBlock in this paper.

Click-Through Rate Prediction Recommendation Systems

BoostingBERT:Integrating Multi-Class Boosting into BERT for NLP Tasks

no code implementations13 Sep 2020 Tongwen Huang, Qingyun She, Junlin Zhang

Our proposed model uses the pre-trained Transformer as the base classifier to choose harder training sets to fine-tune and gains the benefits of both the pre-training language knowledge and boosting ensemble in NLP tasks.

Ensemble Learning Knowledge Distillation

GateNet: Gating-Enhanced Deep Network for Click-Through Rate Prediction

1 code implementation6 Jul 2020 Tongwen Huang, Qingyun She, Zhiqiang Wang, Junlin Zhang

Inspired by these observations, we propose a novel model named GateNet which introduces either the feature embedding gate or the hidden gate to the embedding layer or hidden layers of DNN CTR models, respectively.

Click-Through Rate Prediction

Correct Normalization Matters: Understanding the Effect of Normalization On Deep Neural Network Models For Click-Through Rate Prediction

no code implementations23 Jun 2020 Zhiqiang Wang, Qingyun She, PengTao Zhang, Junlin Zhang

Normalization has become one of the most fundamental components in many deep neural networks for machine learning tasks while deep neural network has also been widely used in CTR estimation field.

Click-Through Rate Prediction

FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction

3 code implementations23 May 2019 Tongwen Huang, Zhiqi Zhang, Junlin Zhang

In this paper, a new model named FiBiNET as an abbreviation for Feature Importance and Bilinear feature Interaction NETwork is proposed to dynamically learn the feature importance and fine-grained feature interactions.

Click-Through Rate Prediction Feature Importance

FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine

1 code implementation15 May 2019 Junlin Zhang, Tongwen Huang, Zhiqi Zhang

Although some CTR model such as Attentional Factorization Machine (AFM) has been proposed to model the weight of second order interaction features, we posit the evaluation of feature importance before explicit feature interaction procedure is also important for CTR prediction tasks because the model can learn to selectively highlight the informative features and suppress less useful ones if the task has many input features.

Click-Through Rate Prediction Feature Importance +1

Empirical Evaluation of RNN Architectures on Sentence Classification Task

no code implementations29 Sep 2016 Lei Shen, Junlin Zhang

Recurrent Neural Networks have achieved state-of-the-art results for many problems in NLP and two most popular RNN architectures are Tail Model and Pooling Model.

Classification General Classification +1

Online classifier adaptation for cost-sensitive learning

no code implementations23 Mar 2015 Junlin Zhang, Jose Garcia

To solve this problem, we propose to learn a new classifier by adding an adaptation function to the base classifier, and update the adaptation function parameter according to the streaming data samples.

Classification General Classification

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