Features

Overview

With the great success of deep learning,DNN-based techniques have been widely used in CTR prediction task.

DNN based CTR prediction models usually have following 4 modules: Input,Embedding,Low-order&High-order Feature Extractor,Prediction

• Input&Embedding

The data in CTR estimation task usually includes high sparse,high cardinality categorical features and some dense numerical features.

Since DNN are good at handling dense numerical features,we usually map the sparse categorical features to dense numerical through embedding technique.

For numerical features,we usually apply discretization or normalization on them.

• Feature Extractor

Low-order Extractor learns feature interaction through product between vectors.Factorization-Machine and it’s variants are widely used to learn the low-order feature interaction.

High-order Extractor learns feature combination through complex neural network functions like MLP,Cross Net,etc.

Feature Columns

SparseFeat

SparseFeat is a namedtuple with signature SparseFeat(name, vocabulary_size, embedding_dim, use_hash, dtype, embeddings_initializer, embedding_name, group_name, trainable)

• name : feature name
• vocabulary_size : number of unique feature values for sprase feature or hashing space when use_hash=True
• embedding_dim : embedding dimension
• use_hash : defualt False.If True the input will be hashed to space of size vocabulary_size.
• dtype : default int32.dtype of input tensor.
• embeddings_initializer : initializer for the embeddings matrix.
• embedding_name : default None. If None, the embedding_name will be same as name.
• group_name : feature group of this feature.
• trainable: default True.Whether or not the embedding is trainable.

DenseFeat

DenseFeat is a namedtuple with signature DenseFeat(name, dimension, dtype, transform_fn)

• name : feature name
• dimension : dimension of dense feature vector.
• dtype : default float32.dtype of input tensor.
• transform_fn : If not None , a function that can be used to transform values of the feature. the function takes the input Tensor as its argument, and returns the output Tensor. (e.g. lambda x: (x - 3.0) / 4.2).

VarLenSparseFeat

VarLenSparseFeat is a namedtuple with signature VarLenSparseFeat(sparsefeat, maxlen, combiner, length_name, weight_name,weight_norm)

• sparsefeat : a instance of SparseFeat
• maxlen : maximum length of this feature for all samples
• combiner : pooling method,can be sum,mean or max
• length_name : feature length name,if None, value 0 in feature is for padding.
• weight_name : default None. If not None, the sequence feature will be multiplyed by the feature whose name is weight_name.
• weight_norm : default True. Whether normalize the weight score or not.

Models

CCPM (Convolutional Click Prediction Model)

CCPM can extract local-global key features from an input instance with varied elements, which can be implemented for not only single ad impression but also sequential ad impression.

CCPM Model API CCPM Estimator API

CCPM

Liu Q, Yu F, Wu S, et al. A convolutional click prediction model[C]//Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. ACM, 2015: 1743-1746.

FNN (Factorization-supported Neural Network)

According to the paper,FNN learn embedding vectors of categorical data via pre-trained FM. It use FM’s latent vector to initialiaze the embedding vectors.During the training stage,it concatenates the embedding vectors and feeds them into a MLP(MultiLayer Perceptron).

FNN Model API FNN Estimator API

FNN

Zhang W, Du T, Wang J. Deep learning over multi-field categorical data[C]//European conference on information retrieval. Springer, Cham, 2016: 45-57.

PNN (Product-based Neural Network)

PNN concatenates sparse feature embeddings and the product between embedding vectors as the input of MLP.

PNN Model API PNN Estimator API

PNN

Qu Y, Cai H, Ren K, et al. Product-based neural networks for user response prediction[C]//Data Mining (ICDM), 2016 IEEE 16th International Conference on. IEEE, 2016: 1149-1154.

Wide & Deep

WDL’s deep part concatenates sparse feature embeddings as the input of MLP,the wide part use handcrafted feature as input. The logits of deep part and wide part are added to get the prediction probability.

WDL Model API WDL Estimator API

WDL

Cheng H T, Koc L, Harmsen J, et al. Wide & deep learning for recommender systems[C]//Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. ACM, 2016: 7-10.

DeepFM

DeepFM can be seen as an improvement of WDL and FNN.Compared with WDL,DeepFM use FM instead of LR in the wide part and use concatenation of embedding vectors as the input of MLP in the deep part. Compared with FNN,the embedding vector of FM and input to MLP are same. And they do not need a FM pretrained vector to initialiaze,they are learned end2end.

DeepFM Model API DeepFM Estimator API

DeepFM

Guo H, Tang R, Ye Y, et al. Deepfm: a factorization-machine based neural network for ctr prediction[J]. arXiv preprint arXiv:1703.04247, 2017.

MLR(Mixed Logistic Regression/Piece-wise Linear Model)

MLR can be viewed as a combination of $2m$ LR model, $m$ is the piece(region) number. $m$ LR model learns the weight that the sample belong to each region,another m LR model learn sample’s click probability in the region. Finally,the sample’s CTR is a weighted sum of each region’s click probability.Notice the weight is normalized weight.

MLR Model API

MLR

Gai K, Zhu X, Li H, et al. Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction[J]. arXiv preprint arXiv:1704.05194, 2017.

NFM (Neural Factorization Machine)

NFM use a bi-interaction pooling layer to learn feature interaction between embedding vectors and compress the result into a singe vector which has the same size as a single embedding vector. And then fed it into a MLP.The output logit of MLP and the output logit of linear part are added to get the prediction probability.

NFM Model API NFM Estimator API

NFM

He X, Chua T S. Neural factorization machines for sparse predictive analytics[C]//Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM, 2017: 355-364.

AFM (Attentional Factorization Machine)

AFM is a variant of FM,tradional FM sums the inner product of embedding vector uniformly. AFM can be seen as weighted sum of feature interactions.The weight is learned by a small MLP.

AFM Model API AFM Estimator API

AFM

Xiao J, Ye H, He X, et al. Attentional factorization machines: Learning the weight of feature interactions via attention networks[J]. arXiv preprint arXiv:1708.04617, 2017.

DCN (Deep & Cross Network)

DCN use a Cross Net to learn both low and high order feature interaction explicitly,and use a MLP to learn feature interaction implicitly. The output of Cross Net and MLP are concatenated.The concatenated vector are feed into one fully connected layer to get the prediction probability.

DCN Model API DCN Estimator API

DCN

Wang R, Fu B, Fu G, et al. Deep & cross network for ad click predictions[C]//Proceedings of the ADKDD’17. ACM, 2017: 12.

DCN-Mix (Improved Deep & Cross Network with mix of experts and matrix kernel)

DCN-Mix uses a matrix kernel instead of vector kernel in CrossNet compared with DCN,and it uses mixture of experts to learn feature interactions.

DCN-Mix Model API

DCN-Mix

Wang R, Shivanna R, Cheng D Z, et al. DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems[J]. arXiv preprint arXiv:2008.13535, 2020.

DIN (Deep Interest Network)

DIN introduce a attention method to learn from sequence(multi-valued) feature. Tradional method usually use sum/mean pooling on sequence feature. DIN use a local activation unit to get the activation score between candidate item and history items. User’s interest are represented by weighted sum of user behaviors. user’s interest vector and other embedding vectors are concatenated and fed into a MLP to get the prediction.

DIN Model API

DIN example

DIN

Zhou G, Zhu X, Song C, et al. Deep interest network for click-through rate prediction[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2018: 1059-1068.

DIEN (Deep Interest Evolution Network)

Deep Interest Evolution Network (DIEN) uses interest extractor layer to capture temporal interests from history behavior sequence. At this layer, an auxiliary loss is proposed to supervise interest extracting at each step. As user interests are diverse, especially in the e-commerce system, interest evolving layer is proposed to capture interest evolving process that is relative to the target item. At interest evolving layer, attention mechanism is embedded into the sequential structure novelly, and the effects of relative interests are strengthened during interest evolution.

DIEN Model API

DIEN example

DIEN

Zhou G, Mou N, Fan Y, et al. Deep Interest Evolution Network for Click-Through Rate Prediction[J]. arXiv preprint arXiv:1809.03672, 2018.

xDeepFM

xDeepFM use a Compressed Interaction Network (CIN) to learn both low and high order feature interaction explicitly,and use a MLP to learn feature interaction implicitly. In each layer of CIN,first compute outer products between $x^k$ and $x_0$ to get a tensor $Z_{k+1}$,then use a 1DConv to learn feature maps $H_{k+1}$ on this tensor. Finally,apply sum pooling on all the feature maps $H_k$ to get one vector.The vector is used to compute the logit that CIN contributes.

xDeepFM Model API xDeepFM Estimator API

CIN

xDeepFM

Lian J, Zhou X, Zhang F, et al. xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems[J]. arXiv preprint arXiv:1803.05170, 2018.

AutoInt(Automatic Feature Interaction)

AutoInt use a interacting layer to model the interactions between different features. Within each interacting layer, each feature is allowed to interact with all the other features and is able to automatically identify relevant features to form meaningful higher-order features via the multi-head attention mechanism. By stacking multiple interacting layers,AutoInt is able to model different orders of feature interactions.

AutoInt Model API AutoInt Estimator API

InteractingLayer

AutoInt

Song W, Shi C, Xiao Z, et al. Autoint: Automatic feature interaction learning via self-attentive neural networks[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 2019: 1161-1170.

ONN(Operation-aware Neural Networks for User Response Prediction)

ONN models second order feature interactions like like FFM and preserves second-order interaction information as much as possible.Further more,deep neural network is used to learn higher-ordered feature interactions.

ONN Model API

ONN

Yang Y, Xu B, Shen F, et al. Operation-aware Neural Networks for User Response Prediction[J]. arXiv preprint arXiv:1904.12579, 2019.

FGCNN(Feature Generation by Convolutional Neural Network)

FGCNN models with two components: Feature Generation and Deep Classifier. Feature Generation leverages the strength of CNN to generate local patterns and recombine them to generate new features. Deep Classifier adopts the structure of IPNN to learn interactions from the augmented feature space.

FGCNN Model API

FGCNN

Liu B, Tang R, Chen Y, et al. Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction[J]. arXiv preprint arXiv:1904.04447, 2019.

DSIN(Deep Session Interest Network)

Deep Session Interest Network (DSIN) extracts users’ multiple historical sessions in their behavior sequences. First it uses self-attention mechanism with bias encoding to extract users’ interests in each session. Then apply Bi-LSTM to model how users’ interests evolve and interact among sessions. Finally, local activation unit is used to adaptively learn the influences of various session interests on the target item.

DSIN Model API

DSIN example

DSIN

Feng Y, Lv F, Shen W, et al. Deep Session Interest Network for Click-Through Rate Prediction[J]. arXiv preprint arXiv:1905.06482, 2019.

BST(Behavior Sequence Transformer)

BST use the powerful Transformer model to capture the sequential signals underlying users’ behavior sequences .

BST Model API

BST example

BST

Qiwei Chen, Huan Zhao, Wei Li, Pipei Huang, and Wenwu Ou. 2019. Behavior sequence transformer for e-commerce recommendation in Alibaba. In Proceedings of the 1st International Workshop on Deep Learning Practice for High-Dimensional Sparse Data (DLP-KDD ‘19). Association for Computing Machinery, New York, NY, USA, Article 12, 1–4. DOI:)

FiBiNET(Feature Importance and Bilinear feature Interaction NETwork)

Feature Importance and Bilinear feature Interaction NETwork is proposed to dynamically learn the feature importance and fine-grained feature interactions. On the one hand, the FiBiNET can dynamically learn the importance of fea- tures via the Squeeze-Excitation network (SENET) mechanism; on the other hand, it is able to effectively learn the feature interactions via bilinear function.

FiBiNET Model API FiBiNET Estimator API

FiBiNET

Huang T, Zhang Z, Zhang J. FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction[J]. arXiv preprint arXiv:1905.09433, 2019.

FLEN(Field-Leveraged Embedding Network)

A large-scale CTR prediction model with efficient usage of field information to alleviate gradient coupling problem.

FLEN Model API

FLEN example

FLEN

Chen W, Zhan L, Ci Y, Lin C. FLEN: Leveraging Field for Scalable CTR Prediction[J]. arXiv preprint arXiv:1911.04690, 2019.

IFM(Input-aware Factorization Machine)

IFM improves FMs by explicitly considering the impact of each individual input upon the representation of features, which learns a unique input-aware factor for the same feature in different instances via a neural network.

IFM Model API

IFM

Yu Y, Wang Z, Yuan B. An Input-aware Factorization Machine for Sparse Prediction[C]//IJCAI. 2019: 1466-1472.

DIFM(Dual Input-aware Factorization Machine)

Dual Input-aware Factorization Machines (DIFMs) can adaptively reweight the original feature representations at the bit-wise and vector-wise levels simultaneously. DIFM Model API

DIFM

Lu W, Yu Y, Chang Y, et al. A Dual Input-aware Factorization Machine for CTR Prediction[C]//IJCAI. 2020: 3139-3145.

DeepFEFM(Deep Field-Embedded Factorization Machine)

FEFM learns symmetric matrix embeddings for each field pair along with the usual single vector embeddings for each feature. FEFM has significantly lower model complexity than FFM and roughly the same complexity as FwFM. DeepFEFM Model API

DeepFEFM

Pande H. Field-Embedded Factorization Machines for Click-through rate prediction[J]. arXiv preprint arXiv:2009.09931, 2020.

Layers

The models of deepctr are modular, so you can use different modules to build your own models.

The module is a class that inherits from tf.keras.layers.Layer,it has the same attributes and methods as keras Layers like tf.keras.layers.Dense() etc

You can see layers API in Layers