# AutoFT: Automatic Fine-Tune for Parameters Transfer Learning in Click-
Through Rate Prediction

Xiangli Yang1 111This work was done when Xiangli Yang was an intern at Huawei
Noah’s Ark Lab., Qing Liu2, Rong Su2, Ruiming Tang2, Zhirong Liu2, Xiuqiang
He2 1Chongqing Jiaotong University
2Huawei Noah’s Ark Lab<EMAIL_ADDRESS><EMAIL_ADDRESS>
###### Abstract

Recommender systems are often asked to serve multiple recommendation scenarios
or domains. Fine-tuning a pre-trained CTR model from source domains and
adapting it to a target domain allows knowledge transferring. However,
optimizing all the parameters of the pre-trained network may result in over-
fitting if the target dataset is small and the number of parameters is large.
This leads us to think of directly reusing parameters in the pre-trained model
which represent more general features learned from multiple domains. However,
the design of freezing or fine-tuning layers of parameters requires much
manual effort since the decision highly depends on the pre-trained model and
target instances. In this work, we propose an end-to-end transfer learning
framework, called Automatic Fine-Tuning (AutoFT), for CTR prediction. AutoFT
consists of a field-wise transfer policy and a layer-wise transfer policy. The
field-wise transfer policy decides how the pre-trained embedding
representations are frozen or fine-tuned based on the given instance from the
target domain. The layer-wise transfer policy decides how the high-order
feature representations are transferred layer by layer. Extensive experiments
on two public benchmark datasets and one private industrial dataset
demonstrate that AutoFT can significantly improve the performance of CTR
prediction compared with stateof-the-art transferring approaches.

##  1 Introduction

Modern recommender systems are widely deployed to make precise personalized
recommendations. In recommender systems, Click-Through Rate (CTR) prediction
is a crucial task, which estimates the probability that a user will click on a
recommended item under a specific context, so that the recommendation
decisions can be made based on the predicted CTR values Cheng et al. (2016);
Guo et al. (2017); Qu et al. (2016, 2018); Lian et al. (2018); Wang et al.
(2017). Recommender systems are often asked to serve multiple recommendation
scenarios. However, designing a unique CTR prediction model for each scenario
(i.e., domain or task) is hard to achieve due to the limit of computing
resource and human resource. Making it even worse, training a model on a
domain with little data often leads to over-fitting. A common practical
solution is to integrate data from different domains but with similar
distributions and train a “general” model. Then this “general” model is
deployed to serve different scenarios. The drawback of this solution is
obvious, i.e., this “general” model is not the optimal for every and each
target domain.

Figure 1: Introduction of parameters transfer learning of CTR model. This is
an example showing that different instances in the target domain may need
different amount of source information to fine-tune the model parameters.

Transfer learning is a promising way of helping train a more precise model for
each target domain. In this work, we deal with the task of transferring the
“general” CTR model trained from the source domains to a target domain222We
focus on the case of only one target domain. When there exist multiple target
domains, we transfer the “general” CTR model to each target domain
individually.. In particular, we focus on deep CTR models as the “general”
model, pre-trained in a supervised manner on the source domains. To do so, we
need to tackle the following challenges.

(C1) Existing recommendation literature is unclear on whether deep CTR models
can be transferred from source domains to target domains where different
domains may not share users and items. Moreover, there are various deep CTR
models in the literature. Therefore, a transfer learning framework that is
compatible with any deep CTR model is needed. (C2) Deep CTR models follow an
_embedding_ & _feature interaction_ paradigm. It is hard to determine whether
and how each of these two modules are transferred to a target domain, so that
the model performance on the target domain is superior. (C3) Different
training instances and features may need different amount of information from
source domains as shown in Figure 1. For example, items that are less frequent
in the target domain may need more information from the source domains than
the more popular items in the target domain. Such fine granularity control of
knowledge transfer is hardly possible to manage by experts.

Transfer learning and domain adaptation techniques are well studied in
computer vision (CV) area Yosinski et al. (2014); Rusu et al. (2016); Xuhong
et al. (2018); Guo et al. (2019) and natural language processing (NLP) area
Devlin et al. (2019); Ruder et al. (2019), but not much in CTR prediction
area. The typical way of performing transfer learning with deep neural
networks is to fine-tune all the parameters of a pre-trained model on the
source domain using data from the target domain. However, it is unclear
whether fine-tuning all the parameters for all the instances in the target
domain is the optimal solution. These works Yosinski et al. (2014); Rusu et
al. (2016); Xuhong et al. (2018); Guo et al. (2019) proposed in CV area
suggest to import the pre-trained model information wisely. For example,
ProgressiveNN (Progrssive Neural Network) Rusu et al. (2016) proposes to
combine the parameters of the pre-trained network and the fine-tuned network
via MLP. $L^{2}$-SP Xuhong et al. (2018) investigates several regularization
schemes and recommends a simple $L^{2}$ penalty to encourage the similarity of
the pre-trained network and the fine-tuned network. SpotTune Guo et al. (2019)
treats instances differently during fine-tuning by training a policy network
to decide which layer of a model should be frozen or fine-tuned for individual
instances. However, these works cannot resolve C2 and C3 if we apply them
directly in CTR prediction area, since they either do not distinguish
different components of a deep CTR model (such as Guo et al. (2019)) or do not
consider different network layers and individual instances (like Xuhong et al.
(2018); Rusu et al. (2016)) during the transfer learning. More importantly,
these models are designed for CV where the input are continuous values that
represent quantifiable values of images, which is quite different from CTR
prediction area.

In this paper, we explore how transferable are parameters in a pre-trained CTR
prediction model, which usually consists of an embedding layer and a feature
interaction layer, from multiple source domains to a target domain. We mainly
focus on transferring the parameters of a pre-trained model based on source
domains with proper fine-tuning. Other transfer learning approaches such as
instance based approaches Lin et al. (2013); Zhang et al. (2014); Wang et al.
(2019); Zhong et al. (2020), feature based approaches Pan (2010); Pan et al.
(2008) and domain adaptation approaches Ganin and Lempitsky (2015); Long et
al. (2016); Aggarwal et al. (2019); Tran et al. (2019); Liu et al. (2020) are
beyond the scope of this work.

We propose an adaptive fine-tuning approach for transferring learning on deep
CTR models, called AutoFT, which automatically finds a route between the pre-
trained network and the siamese fine-tuning network per instance in the target
domain. Exemplary routes are depicted in red color in Figure 2 . In
particular, to cope C1, AutoFT is a general framework to import parameters
from the pre-trained model, which is compatible to various deep CTR models in
the literature. Without lose of generality, we use DCN Wang et al. (2017)
model to elaborate the details of AutoFT. For C2, different transfer
strategies are designed in AutoFT for different components of a deep CTR
model, since different components play unique roles in CTR prediction.
Specifically, we propose a _field-wise_ transfer policy for the embedding
layer and a _layer-wise_ transfer policy for the feature interaction layers.
To handle C3, we design a set of instanced-based policy networks to decide
which parameters should be preserved as in the pre-trained model (frozen) or
fine-tuned, so that feasible parameter adaptation for different instances and
features is achieved.

In summary, we make the following contributions in this paper.

  * •

Firstly, we propose a framework AutoFT for transferring parameters of a pre-
trained CTR prediction model from source domains to a target domain. AutoFT
automatically finds a route between the pre-trained network and the siamese
fine-tuning network per instance in the target domain and is compatible with
any deep CTR models.

  * •

We propose a field-wise transfer policy for the embedding layer, and a layer-
wise transfer policy for the feature interaction layers. Feasible parameter
adaption for different features and instances is achieved by designing a set
of learnable policy networks.

  * •

We compare the performance of our propose approach with many state-of-the-art
methods which is also designed for re-using the parameters of pre-trained
models, on two public benchmarks and one private dataset. The experimental
results demonstrate the superiority of our proposed AutoFT in transfer
learning.

  * •

We also study which components or layers of a CTR model need more fine-tuning.
The result shows that there is similar phenomenon to computer vision (CV) that
the lower layers of the network represent more general features while higher
layers need more fine-tuning to fit for a specific target domain.

##  2 Related Work

###  2.1 Deep Models in CTR prediction

Recently, deep learning based models achieve state-of-the-art performance for
CTR prediction Cheng et al. (2016); Guo et al. (2017); Qu et al. (2016); Wang
et al. (2017); Lian et al. (2018). Deep CTR models follow an _embedding_ &
_feature interaction_ paradigm. Many works focus on designing new network
architectures to improve the feature interaction component, aiming to better
capture the nonlinear relationship among features.

Wide & Deep Cheng et al. (2016) proposed by Cheng et al. jointly trains a wide
linear model for manually designed second-order feature interactions and a
deep neural network for higher-order feature interactions. DeepFM Guo et al.
(2017) and PNN Qu et al. (2016) use an FM layer to learn second-order feature
interactions and MLP to learn higher-order feature interactions. PIN Qu et al.
(2018) introduces a network-in-network architecture to model pairwise feature
interactions with sub-networks rather than using simple inner product
operations as in PNN and DeepFM. DIN Zhou et al. (2018) and AFM Xiao et al.
(2017) utilize attention network to differentiate contribution of each second-
order feature interactions. DCN Wang et al. (2017) and xDeepFM Lian et al.
(2018) apply a cross operation and a compressed interaction respectively, to
explicitly learn $p^{\text{th}}$ order feature interactions from
$(p-1)^{\text{th}}$ order ones.

These works mainly focus on designing network architectures for the feature
interaction component of deep CTR models. They achieve good performance in a
single domain. In real-world applications, a well trained model in one or
multiple source domains may be useful for a target domain, because sharing
such information may be beneficial especially when the target domain is of
limited data. In this paper, we study this problem and propose a transfer
learning framework for transferring parameters of a pre-trained CTR model from
source domains to a target domain which is compatible with the above mentioned
deep CTR models.

###  2.2 Transfer Learning

Transfer learning and domain adaptation techniques are widely used in computer
vision while they are relatively rarely explored in the CTR prediction area.
For example, Yosinski et al. Yosinski et al. (2014) experimentally quantify
the generality versus specificity of neurons in each layer of a deep
convolutional neural network. They report that initializing a network with
transferred features from almost any number of layers can boost the
generalization that lingers even after fine-tuning to the target domain. This
result has yet been verified in CTR prediction area where the input feature is
not usually continuous, and the model structure is quite different from
computer vision.

While fine-tuning in Yosinski et al. (2014) incorporates the prior knowledge
only by initializing with a pre-trained model, ProgressiveNN Rusu et al.
(2016) retains the parameters of the pre-trained model and learns another set
of parameters from the pre-trained model via fine-tuning. Then, the pre-
trained parameters and fine-tuned parameters are combined via MLP, which is
proven to be effective for tansfer learning. $L^{2}$-SP Xuhong et al. (2018)
assumes that the pre-trained model from source domains are useful for the
target model. This work explores several regularization schemes that
explicitly promote the similarity between the fine-tuned model on the target
domain and the pre-trained model from the source domains. They find that a
simple $L^{2}$ regularization performs best. SpotTune Guo et al. (2019)
proposes an adaptive fine-tuning approach which aims to find the optimal fine-
tuning strategy for each instance in the target domain. A policy network is
learned to make routing decisions on whether to pass through the fine-tuned
layers or through the pre-trained layers. More recently, PeterRecYuan et al.
(2020) keeps the pre-trained parameters unaltered, while introduces several
network layers with learnable parameters.

Our work is inspired by Guo et al. (2019). However, we mainly focus on
exploring the parameter transferability in different components of deep CTR
models which have significantly different input features and structures
compared with models in computer vision. Such differences bring challenges
when we transfer deep CTR models from source domains to the target domain, as
stated in Section 1.

###  2.3 Cross Domain Recommendation

There are some work for cross domain recommendation that are relevant to our
work. For example, CST Pan et al. (2010), NATR Gao et al. (2019), DARecYuan et
al. (2019) focus on transferring feature representations, such as user/item
embeddings, from source domains to target domains. Those methods are similar
to model-based methods if we take the embeddings as part of the CTR model.
However, these methods do not tell us whether other parts of a CTR model are
transferable or not. We compare AutoFT with NATR in the experiments.
Approaches such as CST and DARec that rely on the availability of rating
matrix is beyond the scope of this work.

##  3 Methodology

Figure 2: Framework of AutoFT compared to traditional pre-train and fine-tune
strategies. Noted that the fine-tune parts share same color in Fine-Tune Model
and AutoFT, but they have different parameters because of different training
way.

In this section, we introduce our proposed method AutoFT, which is an
automatic fine-tuning strategy that decides which layers of a deep CTR model
and which input fields should be frozen or fine-tuned for each target
instance. AutoFT is compatible to any deep CTR models. With the loss of
generality and for the ease of presentation, we select a representative CTR
model, i.e., Deep & Cross Network (DCN) Wang et al. (2017) as the backbone
model for our fine-tuning method AutoFT. For clarity, we start by introducing
the backbone DCN model in Section 3.1. Then, the details of the proposed
AutoFT framework are presented in Section 3.2. Finally, in Section 3.3, we
elaborate learning the policy network in AutoFT by using the Gumbel Softmax
trick.

###  3.1 Backbone DCN Model

As stated earlier, most deep CTR models follow an embedding & feature
interaction paradigm, which consists of embedding layer, feature interaction
layers and prediction layer. DCN, introduced by Wang et al. Wang et al.
(2017), also an instantiation of this paradigm. We present the details of DCN
model in this section.

Embedding layer. In CTR prediction, one-hot or multi-hot encoding is used to
represent an input instance, as in Eq.(1), which includes multiple categorical
fields, such as item category.

$\mathbf{x}_{\text{hot}}=[\mathbf{x}_{\text{hot},1},\mathbf{x}_{\text{hot},2},\cdots,\mathbf{x}_{\text{hot},m}],$
(1)

where $m$ is the number of fields and $\mathbf{x}_{\text{hot},i}$ is the one-
hot or multi-hot encoding vector of the $i$-th field in this instance. Each
field consists of possibly many feature values, therefore the one-hot encoding
vector $\mathbf{x}_{\text{hot}}$ is a high-dimensional sparse binary vector.
To reduce the dimensionality, the embedding layer transforms the binary one-
hot vectors into dense low-dimensional vectors, as in Eq.(2).

$\mathbf{x}_{i}=\mathbf{V}_{i}\mathbf{x}_{\text{hot},i},$ (2)

where $\mathbf{x}_{i}$ is the embedding vector of $\mathbf{x}_{\text{hot},i}$,
$\mathbf{V}_{i}\in\mathbb{R}^{k\times n_{i}}$ is the corresponding embedding
matrix of the $i$-th field, and $k,n_{i}$ are the embedding size and number of
features in the $i$-th field. The embedding matrices of all the fields are
optimized together with network parameters.

For a multivariate field, the representation of the multi-hot vector
$\mathbf{x}_{\text{hot},i}$ with $k$ elements
$\mathbf{x}_{i}[j]=1~{}(j=i_{1},i_{2},\cdots,i_{k})$ is the average of the
individual embeddings:

$\mathbf{x}_{i}=\frac{1}{k}\sum_{j=i_{1}}^{i_{k}}\mathbf{V}_{i}\mathbf{x}_{\text{hot},i}[j].$
(3)

The final output of the embedding layer is the concatenation of embedding
vectors of all the fields:

$\mathbf{x}=[\mathbf{x}_{1},\mathbf{x}_{2},\cdots,\mathbf{x}_{m}].$ (4)

Feature Interaction layers. The key challenge of CTR prediction is to model
feature interactions effectively. In DCN model, the feature interaction layers
consist of a _cross network_ and a _deep network_.

The cross network applies explicit feature interactions in an efficient way.
It consists of multiple layers, where each layer produces higher-order
interactions based on existing ones, and keeps the interactions from previous
layers. Let $\mathbf{x}^{(l)}$ denote the $l$-th cross layer. The output cross
layer $\mathbf{x}^{(l+1)}$ is generated as in Eq.(5).

$\mathbf{x}^{(l+1)}=\mathbf{x}^{(0)}\mathbf{x}^{(l)\text{T}}\mathbf{w}_{\text{c}}^{(l)}+\mathbf{b}_{\text{c}}^{(l)}+\mathbf{x}^{(l)},$
(5)

where $\mathbf{w}_{\text{c}}^{(l)}$ and $\mathbf{b}_{\text{c}}^{(l)}$ are the
weight and bias terms of the $l$-th cross layer, and $\mathbf{x}^{(0)}$ is
first layer of cross network (which is equivalent to the output of embedding
layer).

The deep network, in parallel with the cross network, is a fully-connected
neural network, introduced to capture high-order nonlinear feature
interactions. Let $\mathbf{h}^{(l)}$ denote the $l$-th deep layer, the output
deep layer $\mathbf{h}^{(l+1)}$ is shown in Eq.(6).

$\mathbf{h}^{(l+1)}=\text{ReLU}(\mathbf{w}_{\text{d}}^{(l)}\mathbf{h}^{(l)}+\mathbf{b}_{\text{d}}^{(l)}),$
(6)

where $\mathbf{w}_{\text{d}}^{(l)}$ and $\mathbf{b}_{\text{d}}^{(l)}$ are
weight and bias terms for the $l$-th deep layer. ReLU is an activation
function.

Prediction layer. The prediction layer combines the outputs from the cross
network and the deep network, i.e., $\mathbf{x}^{(L_{\text{c}})}$ and
$\mathbf{h}^{(L_{\text{d}})}$ to make prediction, where $L_{\text{c}}$ and
$L_{\text{d}}$ represent the number of layers in the cross network and the
deep network. More specifically, the prediction is based on the concatenation
of the two output vectors as in Eq.(7).

$\hat{y}=\text{Sigmoid}(\mathbf{w}_{\text{o}}[\mathbf{x}^{(L_{\text{c}})},\mathbf{h}^{(L_{\text{d}})}]+\mathbf{b}_{\text{o}}),$
(7)

where $\mathbf{w}_{\text{o}},\mathbf{b}_{\text{o}}$ are the weight and bias
terms for the prediction layer. Sigmoid is an activation function.

The loss function is cross-entropy of the predicted values $\hat{y}$ and the
labels $y$ with a regularization term,

$\mathcal{L}(y,\hat{y})=-y\log(\hat{y})-(1-y)\log(1-\hat{y})+\lambda\lVert\mathbb{W}\rVert^{2},$
(8)

where $\mathbb{W}$ represents the set of parameters of DCN model, and
$\lambda$ is a hyper-parameter to balance the prediction error and the
regularization term.

###  3.2 AutoFT Framework

The key idea of AutoFT is to train a set of learnable policy networks, one of
which is to decide which fields of the input instance should be frozen or
fine-tuned (referred as field-wise transfer policy), while the others make
routing decisions on whether to pass the input through the pre-trained network
layers or the siamese fine-tuning network layers (referred as layer-wise
transfer policy). Following the definition of transfer learning Cao et al.
(2010), the source domains are defined as $\mathcal{S}$ and the target domain
is represented as $\mathcal{T}$. The key notations of the pre-trained and
fine-tuned networks are shown in Tabel 1.

Table 1: Related notations of the pre-trained and fine-tune model. Notation | Meaning
---|---
$\mathbf{V}^{\mathcal{S}}$ | pre-trained embedding matrix in source domain
$\mathbf{V}^{\mathcal{T}}$ | fine-tuned embedding matrix in target domain
$\mathbf{x}^{\mathcal{S}}$ | embedding vector of a source instance
$\mathbf{x}^{\mathcal{T}}$ | embedding vector of a target instance
$\mathbf{w}_{\text{c}}^{\mathcal{S}}$, $\mathbf{b}_{\text{c}}^{\mathcal{S}}$ | pre-trained weight and bias terms of the cross
network in source domain
$\mathbf{w}_{\text{c}}^{\mathcal{T}}$, $\mathbf{b}_{\text{c}}^{\mathcal{T}}$ | fine-tuned weight and bias terms of the cross
network in target domain
$\mathbf{x}^{(l)\mathcal{S}}$ |  $l$-th cross layer of DCN model in source domain
$\mathbf{x}^{(l)\mathcal{T}}$ |  $l$-th cross layer of DCN model in target domain
$\mathbf{w}_{\text{d}}^{\mathcal{S}}$, $\mathbf{b}_{\text{d}}^{\mathcal{S}}$ | pre-trained weight and bias terms of the deep
network in source domain
$\mathbf{w}_{\text{d}}^{\mathcal{T}}$, $\mathbf{b}_{\text{d}}^{\mathcal{T}}$ | fine-tuned weight and bias terms of the deep
network in target domain
$\mathbf{h}^{(l)\mathcal{S}}$ |  $l$-th deep layer of DCN model in source domain
$\mathbf{h}^{(l)\mathcal{T}}$ |  $l$-th deep layer of DCN model in target domain
$\mathbb{W}^{\mathcal{S}}$ | parameters of pre-trained DCN neural network
in source domain
$\mathbb{W}^{\mathcal{T}}$ | parameters of fine-tuned DCN neural network
in target domain

Figure 2 presents the pre-trained CTR model, the fine-tuned CTR model and our
proposed AutoFT framework. The pre-trained CTR model is trained based on the
data from source domains or based on the data from all domains. The fine-tuned
CTR model is initialized by the pre-trained CTR model and fine-tuned with data
from the target domain. AutoFT involves three sets of parameters: the light-
blue boxes contain parameters from the pre-trained CTR model, the dark-blue
boxes contain parameters initialized by the pre-trained model and will be
fine-tuned during training and the red boxes contain the parameters of the
policy networks. Compared to the original DCN network, AutoFT includes both
the pre-trained and fine-tuned network, which has a similar structure as deep
structured semantic models Huang et al. (2013). There are three policy
networks in AutoFT. They decide how parameters are transferred in the
embedding layer, the cross network and deep network, respectively (for
addressing challenge C2). Each policy network is a lightweight neural network,
the input of which is the embedding of each instance such that each instance
has its own fine-tune strategy (for addressing challenge C3). The output
$\mathbf{p}$ of each policy network is a vector of binary values which serves
as the indicator to choose the pre-trained parameters or the fine-tuned
parameters.

$\mathbf{p}=G(\text{ReLU}(\mathbf{W_{p}^{2}}\text{ReLU}(\mathbf{W_{p}^{1}}\mathbf{x}+\mathbf{b_{p}^{1}})+\mathbf{b_{p}^{2}}))$
(9)

Eq.(9) shows an example of the policy network with two fully-connected layers
and a Gumbel-Softmax trick $G$. Details of the Gumbel-Softmax trick will be
illustrated in Section 3.3. Other network structures are also possible for the
policy networks.

Field-wise transfer policy. For the embedding layer, the policy network takes
the pre-trained embedding $\mathbf{x}^{\mathcal{S}}$ from source domains as
input and outputs a _binary_ vector $p_{\text{e}}(\mathbf{x}^{\mathcal{S}})$.
The $i$-th value in this vector, denoted as
$P_{\text{e}}^{(i)}(\mathbf{x}^{\mathcal{S}})$ in Eq.(10), decides whether the
$i$-th field of features should use pre-trained embedding parameters
$\mathbf{V}^{\mathcal{S}}$ or the fine-tuned embedding parameters
$\hat{\mathbf{V}}^{\mathcal{T}}$. $\hat{\mathbf{x}}_{i}^{\mathcal{T}}$
represents feature embeddings where each field is selected either from pre-
trained parameters or from fine-tuned parameters, controlled by the policy
network.

$\hat{\mathbf{x}}_{i}^{\mathcal{T}}=P_{\text{e}}^{(i)}(\mathbf{x}^{\mathcal{S}})\mathbf{V}_{i}^{\mathcal{S}}\mathbf{x}_{\text{hot},i}+(1-P_{\text{e}}^{(i)}(\mathbf{x}^{\mathcal{S}}))\hat{\mathbf{V}}_{i}^{\mathcal{T}}\mathbf{x}_{\text{hot},i}.$
(10)

Noted that $\hat{\mathbf{V}}^{\mathcal{T}}$ and $\mathbf{V}^{\mathcal{T}}$ are
different embedding matrices. $\mathbf{V}^{\mathcal{T}}$ is the embedding
matrix in Fine-Tune model while $\hat{\mathbf{V}}^{\mathcal{T}}$ is the
embedding matrix in AutoFT as shown in Figure 2.

Layer-wise transfer policy. For the cross network and deep network, the input
of the policy network is $\hat{\mathbf{x}}^{\mathcal{T}}$ computed based on
Eq.(10). The outputs are two binary vectors
$P_{\text{c}}(\hat{\mathbf{x}}^{\mathcal{T}})$ and
$P_{\text{d}}(\hat{\mathbf{x}}^{\mathcal{T}})$, to decide a “route” from the
embedding layer to the prediction layer in deep network and cross network,
respectively. Each binary value in each of two vectors determines the
direction of each step in the route, by specifying whether the parameters of
the current layer should inherit from the pre-trained model or from the fine-
tuned model, as shown in Figure 2.

Specifically, in the cross network, the output of $l$-th cross layer is
generated as in Eq.(11).

$\hat{\mathbf{x}}^{(l+1)\mathcal{T}}=P_{\text{c}}^{(l)}(\hat{\mathbf{x}}^{\mathcal{T}})F_{\text{c}}(\hat{\mathbf{x}}^{(l)\mathcal{T}})+(1-P_{\text{c}}^{(l)}(\hat{\mathbf{x}}^{\mathcal{T}}))\hat{F}_{\text{c}}(\hat{\mathbf{x}}^{(l)\mathcal{T}}).$
(11)

$F_{\text{c}}(\cdot)$ is a cross layer formulated in Eq.(5), which take
$\mathbf{x}^{(l)}$ as input and outputs $\mathbf{x}^{(l+1)}$ with frozen pre-
trained parameters $\mathbf{w}_{\text{c}}^{\mathcal{S}}$ and
$\mathbf{b}_{\text{c}}^{\mathcal{S}}$. Similarly, $\hat{F}_{\text{c}}(\cdot)$
is a cross layer with the fine-tuned parameters
$\hat{\mathbf{w}}_{\text{c}}^{\mathcal{T}}$ and
$\hat{\mathbf{b}}_{\text{c}}^{\mathcal{T}}$.

In the deep network, the output of the $l$-th deep layer
$\hat{\mathbf{h}}^{(l+1)\mathcal{T}}$ is generated as in Eq.(12).

$\hat{\mathbf{h}}^{(l+1)\mathcal{T}}=P_{\text{d}}^{(l)}(\hat{\mathbf{x}}^{\mathcal{T}})F_{\text{d}}(\hat{\mathbf{h}}^{(l)\mathcal{T}})+(1-P_{\text{d}}^{(l)}(\hat{\mathbf{x}}^{\mathcal{T}}))\hat{F}_{\text{d}}(\hat{\mathbf{h}}^{(l)\mathcal{T}}).$
(12)

$F_{\text{d}}(\cdot)$ is a deep layer defined in Eq.(6) with frozen pre-
trained parameters $\mathbf{w}_{\text{d}}^{\mathcal{S}}$ and
$\mathbf{b}_{\text{d}}^{\mathcal{S}}$. Similarly, $\hat{F}_{\text{d}}(\cdot)$
is a deep layer with the fine-tuned parameters
$\hat{\mathbf{w}}_{\text{d}}^{\mathcal{T}}$ and
$\hat{\mathbf{b}}_{\text{d}}^{\mathcal{T}}$.

The prediction layer combines the outputs of the cross network and the deep
network to predict the click probability. It has been proven by Yosinski et
al. (2014); Yuan et al. (2020) that the prediction learns features that are
specific to a particular task. Thus, we always fine-tune the parameters of the
prediction layer.

The loss function is the same as the one in Eq.(8). During the training phase,
the parameters from the pre-trained model are frozen, while other parameters
are trainable. During the inference phase, the policy networks generate a
route from the embedding layer to the prediction layer, where each step of the
route passes by either the pre-trained model or the fine-tuned model. Since
the policy networks are lightweight networks, the inference time of AutoFT is
almost the same as DCN.

###  3.3 Gumbel-Softmax Trick

AutoFT makes binary decisions at the embedding layer, the feature interaction
layers (i.e., the cross network and deep network), to choose to inherit
parameters from the pre-trained model or from the fine-tuned model. Such
binary decisions are made by policy networks. The output of the policy
networks are discrete values which make the parameters in the policy networks
non-differentiable. Therefore, it is difficult to optimize the network with
back propagation. The Gumbel-Softmax trick Jang et al. (2016) is an approach
to circumvent this problem. This trick provides a simple and effective way to
draw samples from a categorical distribution parameterized by
$\\{\alpha_{0},\alpha_{2},\cdots,\alpha_{z-1}\\}$, where $\alpha_{i}$ is the
probability of category $i$, and $z$ is the number of categories. In this
paper, there are two categories, $\alpha_{0}$ and $\alpha_{1}$, which are
probabilities of using the frozen pre-trained parameters or the fine-tuned
parameters.

During the forward pass, $z$ samples $G_{0},\cdots,G_{z-1}$ are sampled from a
standard Gumbel distribution $G=-log(-log(u))$, where $u$ is sampled from a
uniform distribution, i.e., $u\sim U[0,1]$. Then each element in a policy
vector $\mathbf{p}$ (corresponding to Eq.(9)) is computed by Eq.(13), where
$\alpha_{i}$ is the output of the fully-connected layers in the policy
networks.

$p_{i}=\mathop{\arg\max}_{i}[\log\alpha_{i}+G_{i}]\quad\text{for
}i=0,\cdots,z-1$ (13)

The argmax in Eq.(13) is non-differentiable. Thus, during the backward pass,
we replace the non-differentiable sample from a categorical distribution with
a differentiable sample from a novel Gumbel-Softmax distribution which can be
smoothly annealed into a categorical distribution. The gradient of the
discrete samples are approximated by computing the gradient of the continuous
softmax relaxation as in Eq.(14).

$Y_{i}=\frac{\exp((\log\alpha_{i}+G_{i})/\tau)}{\sum_{j=1}^{z}\exp((\log\alpha_{j}+G_{j})/\tau)}\quad\text{for
}i=0,\cdots,z-1$ (14)

where $\tau$ is a temperature parameter, which controls the discreteness of
the output vector $Y$. When $\tau$ becomes closer to 0, the samples from the
Gumbel-Softmax distribution become indistinguishable from the discrete
distribution. The Gumbel-Softmax distribution is smooth for $\tau>0$ and
therefore has well-defined gradients with respect to the parameters
$\alpha_{i}$.

By using a standard classification loss for the target domain, the policy
network is jointly trained with the fine-tuned parameters to find the optimal
fine-tune strategy that maximizes the accuracy of CTR prediction in the target
domain. This whole process is illustrated in Figure 2.

##  4 Experiments

In order to verify the effectiveness of the proposed framework AutoFT, we
conduct extensive experiments on two public benchmark datasets and one
industrial dataset. We mainly focus on the following research questions (RQs).

  * •

RQ1: Is AutoFT able to positively transfer the pre-trained model to the target
domain, can it outperform state-of-the-art approaches?

  * •

RQ2: How do different components of proposed AutoFT, i.e., field-wise transfer
policy and layer-wise transfer policy, contribute to the performance?

  * •

RQ3: Is the policy based learning framework AutoFT compatible with any deep
CTR models, such as DNN and DeepFM?

  * •

RQ4: Which layers of the deep CTR models need more fine-tuning?

###  4.1 Datasets

Experiments are conducted on the following two public datasets (MovieLens-1M
and Amazon review data) and one private industrial dataset. The statistics of
the datasets are in Table 2.

Table 2: Statistics of the datasets. We show the number of instances in training, validation, and testing data from source and target domains, respectively, and other important details. Datasets | MovieLens | Amazon | Industrial data
---|---|---|---
Source training | 602,975 | 6,560,984 | 68,129,281
Target training | 197,025 | 2,052,279 | 4,296,836
Source validation | 75,263 | 8,201,231 | 9,264,471
Target validation | 24,737 | 513,069 | 622,236
Source testing | 75,531 | 8,201,231 | 9,259,634
Target testing | 24,678 | 513,069 | 632,941
Ratio of positive | 0.5753 | 0.6040 | 0.0223
Users’ overlap rate | 0.0% | 0.3% | 1.7%
Items’ overlap rate | 100.0% | 0.0% | 78.6%

MovieLens-1M dataset333https://www.grouplens.org/datasets/movielens/: The
dataset includes 1 million movie rating records over thousands of movies and
users Harper and Konstan (2015). The features we used are user id, gender,
occupation, user behavior, movie id, release year, and movie genres. The
ratings greater than 3 are taken as positive samples, and others are taken as
negative samples Volkovs and Yu (2015). The dataset is randomly divided into
the training, validation, and test sets with a ratio of 8:1:1. As we analysed
in the data, the female and male users have different preferences and click
behaviours about movies. Thus, we regard male as the source domain and female
as the target domain.

Amazon review data444https://www.nijianmo.github.io/amazon/index.html: This
dataset contains product reviews and metadata from Amazon, including 24
categories spanning from 1996 to 2018 Ni et al. (2019). The data we used are
$5$-cores records of products in Toys and Games and Video Games. Each of the
users and items have 5 reviews, respectively. Different from MoviLens-1M
dataset, we take ratings greater than 4 as positive samples to balance the
positive samples and negative samples. The features we used are reviewerID,
asin, vote, style and reviewTime. The training, validation, and test sets are
also divided randomly with a ratio of 8:1:1. Records of Toys and Games are
used as source domain since this category has more records than Video Games,
which is more reasonable to transfer general knowledge.

Industrial dataset: This dataset is collected from a real commercial
recommender system deployed in the company X’s App Store. The dataset contains
app features (e.g., ID, category), user features (e.g., user’s click behavior
history) and context features (e.g., time). We choose the two recommendation
scenarios, Must-have Apps and Novel-Fun as the source domain and target
domain, respectively. Each domain contains tens of thousands of apps and more
than ten million users. We collect 73M records from the download logs, which
represent the operations of users to apps. In training process, the source
domain records is used to training the model with the target domain data be
the validation and test dataset to evaluate the model.

###  4.2 Baselines & experimental settings

We compare AutoFT with multiple baseline methods to verify its effectiveness
for CTR prediction. All these methods are evaluated on the testing data from
the target domain.

We firstly introduce three straightforward baseline methods that are easy to
think of without transferring any parameters from the pre-trained model.
Target-only, Source, and All, which use Source-only, and All domains for
training, respectively.

  * •

Target-only is the method to use target domain data for both training and
testing.

  * •

Source-only is to use source domain data for training and use target domain
data for testing.

  * •

All is to use both source and target domain data for training, and use target
domain data for testing.

Then we describe four representative state-of-the-art baseline approaches,
i.e., Fine-tune, ProgressiveNN, $L^{2}$-SP and SpotTune, of transferring
parameters from the pre-trained model. The pre-trained model is learned either
from Source-only or All method.

  * •

Fine-Tune Yosinski et al. (2014) is the method to learn a model which is
initialized by a pre-trained model. Then all the parameters of the model are
fine-tuned using data from the target domain.

  * •

PTU Zhang et al. (2019) is a parameter transfer unit, that produces a weighted
sum of the activations from both the already trained source domain network and
the target domain network.

  * •

ProgressiveNN Rusu et al. (2016) proposes to aggregate the pre-trained
parameters to the fine-tuning parameters with an element-wise non-linear
function. The next layer receives input from both previous pre-trained and
fine-tuning layers via lateral connections.

  * •

$L^{2}$-SP Xuhong et al. (2018) is a regularization-based method for fine-
tuning. A simple $L^{2}$-penalty between the shared pre-trained network
parameters $\mathbb{W}^{\mathcal{S}}$ and the fine-tuning parameters
$\mathbb{W}^{\mathcal{T}}$ is applied to constrain the effective search space
around the initial pre-trained model.

  * •

SpotTune Guo et al. (2019) aims to dynamically route blocks in Residual
Network model, which improves the classification performance in CV. For the
CTR prediction tasks, this method is corresponding to our layer-wise policy
network which is only used to control the choice in the cross and deep
network.

  * •

NATR Gao et al. (2019) resolves the key challenges in leveraging transferred
item embeddings. The two level attention design allows NATR to distill useful
signal from transferred item embeddings, and appropriately combine them with
the data of the target domain.

All methods are implemented with TensorFlow555https://www.tensorflow.org/ and
trained on GPUs (Tesla P100) with Adam optimizerKingma and Ba (2015). All
experiments are repeated five times and the average values are reported. For
the hyper-parameters, we tune learning rate to be from 0.001 to 0.0001, set
batch size to be 8000 and embedding dimension to be 80. Note that, DCN are
used for backbone network in AutoFT. For network architecture, the cross
network has 3 layers iteration, the deep network has 4 layers with dimensions
specifically of 1024, 512, 256, 128. Finally, the common evaluation metrics,
AUC (Area Under ROC) and Logloss (cross-entropy), are used for comparison.

Table 3: Performance comparison of different methods. ∗ denotes statistically significant improvement (measured by t-test with $p$-value$<$0.005) over baselines. Method | MovieLens-1M | Amazon review data | Industrial dataset
---|---|---|---
AUC | LogLoss | AUC | LogLoss | AUC | LogLoss
Target-only | 0.7561 | 0.6791 | 0.7120 | 0.6461 | 0.8949 | 0.03948
Source-only | 0.7112 | 0.6701 | 0.6010 | 0.6549 | 0.8084 | 0.04723
Source-Fine-Tune | 0.7807 | 0.5609 | 0.7128 | 0.6007 | 0.9010 | 0.03907
Source-ProgressiveNN | 0.7817 | 0.5445 | 0.7124 | 0.6023 | 0.9019 | 0.03884
Source-PTU | 0.7809 | 0.5552 | 0.7130 | 0.5999 | 0.9013 | 0.03904
Source-$L^{2}$-SP | 0.7813 | 0.5508 | 0.7131 | 0.5999 | 0.9011 | 0.03905
Source-SpotTune | 0.7814 | 0.5506 | 0.7133 | 0.5997 | 0.9016 | 0.03885
Source-NATR |  / |  / |  / |  / | 0.9014 | 0.03904
Source-AutoFT | 0.7822∗ | 0.5504∗ | 0.7138∗ | 0.5968∗ | 0.9024∗ | 0.03882∗
All | 0.7776 | 0.6137 | 0.7106 | 0.6042 | 0.9039 | 0.03857
All-Fine-Tune | 0.7843 | 0.5430 | 0.7141 | 0.6004 | 0.9040 | 0.03855
All-ProgressiveNN | 0.7832 | 0.5437 | 0.7146 | 0.5965 | 0.9040 | 0.03852
All-PTU | 0.7863 | 0.5415 | 0.7152 | 0.5976 | 0.9039 | 0.03858
All-$L^{2}$-SP | 0.7864 | 0.5407 | 0.7143 | 0.6002 | 0.9046 | 0.03847
All-SpotTune | 0.7885 | 0.5398 | 0.7146 | 0.5980 | 0.9044 | 0.03878
All-NATR |  / |  / |  / |  / | 0.9048 | 0.03846
All-AutoFT | 0.7932∗ | 0.5375∗ | 0.7166∗ | 0.5945∗ | 0.9052∗ | 0.03843∗

###  4.3 Overall Performance (RQ1)

The performance of the baseline approaches and our proposed framework AutoFT
is shown in Table 3. We divide the results into three groups. The first group
shows the results of the basic baseline method Target-only. The second group
of methods are based on a pre-trained model, i.e., Source-only, that is
trained on data from source domains. The third group of methods are based on a
pre-trained model, i.e., All, that is trained on data from all domains. The
best results of the baseline methods in the second and third groups are
underlined. From this table, we have the following observations.

Firstly, AutoFT performs best with respect to AUC among all the approaches on
all datasets no matter that the pre-trained model is based on Source-only or
based on All, which demonstrates the effectiveness of our proposed framework.
Moreover, the state-of-the-art baseline approaches such as ProgressiveNN, PYU,
$L^{2}$-SP, and SpotTune, though do not dominate each other, generally
performs better than the basic baseline approaches such as Target-only,
Source-only, and All. However, their performance is inferior to AutoFT since
they do not distinguish different component of a CTR model, thus, are less
flexible when transferring different amount of valuable information for
different components or instances.

Secondly, we can observe that the approaches that include both source domains
and target domain data perform better than Target-only and perform much better
than Source-only. This observation implies that importing extra data to the
target domain can positively transfer information to improve the prediction
accuracy. For the scenario that share more common items, such as MovieLens-1M
and Industrial data, the benefit of transferring is higher than that share
less common items, such as Amazon Review.

Thirdly, we found that approaches using All as the pre-trained model generally
performs better than approaches using Source-only as the pre-trained model.
This is probably for the reason that All include more “general” features and
parameters than Source-only, which indicates that a more “general” model that
includes information from multiple domains that are relevant to the target
domain can boost the performance of the target domain to a larger margin.

###  4.4 Ablation study (RQ2)

In this section, we study how field-wise transfer policy and layer-wise
transfer policy, which are corresponding to different policy networks in
AutoFT, contribute to the performance. In the experiments, we only keep the
policy network for the embedding layer and remove other policy networks from
All-AutoFT. This method is denoted as Embedding in Table 4. Similarly, Cross,
Deep, Cross & Deep represent the methods that only keep the policy networks
for the cross layers, the deep layers and both the cross layers and deep
layers, respectively.

From Table 4, we can observe that the performance of all variants drops when
we remove certain policy networks from AutoFT which verifies that both field-
wise transfer policy and layer-wise transfer policy play important roles in
the performance. For MovieLens-1M data, the drop of the performance with
respect to AUC of Cross and Deep is significant compared with AutoFT when we
remove field-wise policy network and one of the layer-wise policy networks.
For Amazon data, the drop of their performance is less significant. That
implies for the dataset that shares more items in the source and target
domains, the pre-trained model contains more general information that help the
prediction in the target domain, which should be included by policy networks.

Table 4: The performance of policy net of each component in DCN. Policy-based | MovieLens-1M | Amazon review
---|---|---
Component | AUC | LogLoss | AUC | LogLoss
All-Fine-Tune | 0.7843 | 0.5430 | 0.7141 | 0.6004
Embedding | 0.7878 | 0.5404 | 0.7135 | 0.5995
Cross | 0.7866 | 0.5412 | 0.7136 | 0.5990
Deep | 0.7854 | 0.5432 | 0.7152 | 0.5968
Cross & Deep | 0.7885 | 0.5398 | 0.7146 | 0.5980
All-AutoFT | 0.7932 | 0.5375 | 0.7166 |  0.5945

###  4.5 Compatibility of AutoFT (RQ3)

In this section, we conduct experiments to verify whether AutoFT is compatible
with different deep CTR models. We choose three representative CTR prediction
models for our experiment, they are DCN, DNN and DeepFM. We keep the network
architectures of deep layers of DCN and DNN to be the same which are of size
1024, 512, 256, 128. We set the deep layers of DeepFM to be of size 400, 400,
400 to check if our proposed framework works for a different network size. The
pre-trained model is the baseline All. The experimental results are shown in
Table 5. The difference between All-Fine-Tune and All-AutoFT is that All-Fine-
Tune does not include any transfer policies, all its parameters are fine-
tuned. From Table 5, we can observe that for various deep CTR models, AutoFT
always achieves better results than the Fine-Tune method, which demonstrates
the compatibility of our proposed framework.

Table 5: Compatibility of AutoFT with classic deep CTR models. Model | MovieLens-1M | Amazon review
---|---|---
AUC | LogLoss | AUC | LogLoss
DCN Fine-Tune | 0.7843 | 0.5430 | 0.7141 | 0.6004
DCN AutoFT | 0.7932 | 0.5375 | 0.7166 | 0.5945
DNN Fine-Tune | 0.7754 | 0.6198 | 0.7131 | 0.6002
DNN AutoFT | 0.7840 | 0.5453 | 0.7145 | 0.5987
DeepFM Fine-Tune | 0.7780 | 0.5844 | 0.7120 | 0.5997
DeepFM AutoFT | 0.7852 | 0.5434 | 0.7128 | 0.5991

###  4.6 Visualization of Policy (RQ4)

MovieLens-1M

Amazon review

Figure 3: Visualization of AutoFT. The first row is about deep layers, and the
second row is about the cross layers.

To better understand the fine-tuning decisions learned by the policy networks,
we visualize them on the two public benchmark datasets. For field-wise
transfer policy, we count the number of instances that either using the pre-
trained embedding or the fine-tuned embedding. However, we do not find any
special trend among different fields. For layer-wise transfer policy, we
counted each instance’s layer-wise decision of using pre-trained or fine-tune
parameters during testing. The percentage of the pre-trained and fine-tune
policy in each layer based on MovieLens-1M and Amazon data are shown in Figure
3. For example, a light-blue pre-train bar with a number of 0.2 means 20% of
the instances in the test target domain share the pre-trained parameters in
this layer and the 80% of instances use the fine-tuned parameters. The
visualization shows that different datasets and different layers have specific
fine-tuning policies, which validates our hypothesis that it is more accurate
to have an instance-based fine-tuning policy for CTR prediction. AutoFT allows
us to automatically learn the right policy for each dataset, as well as for
each training instance, without much manual effort. By observing the
percentages of pre-train and fine-tune bars in each sub-figures, we find that
more and more instances choose the fine-tune direction on higher layers. This
also verifies that the lower layers could learn more general features and the
parameters in higher layers are more specific to each domains, which need to
be fine-tuned during transfer learning.

##  5 Conclusion

In this paper, we propose a framework AutoFT to automatically incorporate
parameters from a pre-trained model to facilitate the CTR prediction accuracy
in a new target domain. The field-wise policy in AutoFT decides which fields
of embedding features should be inherited from the pre-trained model or should
be fine-tuned. The layer-wise policy generates route decisions between pre-
trained and fine-tuned parameters for each layer in the deep and cross
networks. To achieve this, lightweight policy networks are jointly trained
with the target domain using a Gumbel-Softmax trick. The proposed framework is
generally applicable to many deep CTR models. Extensive offline experiments
demonstrate the superior performance of AutoFT.

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