DEIM Forum 2019 G1-3

E コマース環境におけるコールドスタート問題のための深層学習に基づ
くクロスドメイン推薦システムに関する検討
Hanxin WANG† , Daichi AMAGATA† , Takuya MAEKAWA† , Takahiro HARA† , Hao NIU†† , Kei
YONEKAWA†† , and Mori KUROKAWA††† Graduate School of Information Science and Technology, Osaka University
1-5, Yamadaoka, Suita, Osaka, 565-0871, Japan†† Integrated Analysis Platform Laboratory, KDDI Research, Inc.
Chiyoda-ku, Tokyo, 102-8460, Japan
E-mail: †{wang.hanxin,amagata.daichi,maekawa,hara}@ist.osaka-u.ac.jp,††{ha-niu,ke-yonekawa,mo-kurokawa}@kddi-research.jp
Abstract 多くのアプリケーションでは，ユーザの嗜好を予測する推薦システムを用いており，カスタマーエクスペ
リエンスや売上の向上に繋げている．しかし，推薦システムは，サービスを利用したことがない新規ユーザへの商品の提案が困難という問題（コールドスタート問題）を抱えている．本研究では，この問題を解決するため，オンライ
ンショッピングと広告プラットフォームの両ドメインのブリッジユーザに基づいて，深層学習によりクロスドメイン
推薦システムを構築する．評価実験により本システムがコールドスタート問題の解決策の一つになり得ることを示す。

Key words Recommender Systems, Cross-domain Recommendation, Deep Learning, Implicit Feedback, E-commerce, Online advertising
methods and deep learning based methods suﬀer from the

1. Introductionuser cold-start problem, which means that it is diﬃcult toIn the era of information explosion, it is hard for users of

perform accurate recommendation when a new user comes,

online shopping services to explore all available products inin particular for recommending items to users who have neverlimited amount of time. Thus, users prefer to get suggestions

used the service before.

of products they might like to buy directly from online ven-

It is important to note that situations where a user partic-

dors. Because of that, recommender systems have become

ipates in some diﬀerent services are getting more and more

an important part in the e-commerce field, and have also

common. For example, a user buys products in an online

been widely adopted by many other online services like on-

shopping site and reads news in other websites. One eﬀec-

line news [1], video-sharing websites [2] and music streaming

tive solution for the user cold-start problem is to transfer theplatforms [3].

knowledge from relevant domains and build a cross-domain

The key part of the personalized recommender systems,

recommender system. Because online shopping sites are al-which is to model users’ preferences on items based on theirways combined with Ad platforms to promote the products

past interactions such as purchase history, is collaborative

empirically, it is not diﬃcult to collect a large amount offiltering (CF) [4]. The most popular collaborative filteringbrowsing history in the Ad domain of users who have bought

technique is matrix factorization (MF) [5, 6], which projects

products in the online shopping site. We would like to callusers and items to a shared latent space by factorizing ausers who have both purchase history in online shopping do-user-item interaction matrix. As the immense success on

main and browsing history in Ad domain as bridge users incomputer vision and natural language processing yielded bythis paper.

deep learning, some recent works have also employed deep

Motivated by the above observation, we use a method toneural networks (DNNs) for recommendation, such as neuralrepresent users by their browsing histories in one Ad plat-

collaborative filtering [7] or using neural networks to model

form, and learn user-item relationship in one online shop-

auxiliary information (e.g., Word2vec [8]). With the pow-

ping service based on bridge users’ purchase history to im-erful ability to learn a high-order nonlinear function, deep

prove the recommendation performance on new users of on-neural networks are suitable to learn complex relationshipline shopping service with browsing history in Ad platform.

between users and items.

We can even accurately recommend products of online shop-

However, both traditional MF

ping sites to latent users who have never used the service

is studied mainly for new items (items which have no in-

before through ad distribution. We apply diﬀerent kinds ofteractions with any users). This kind of works primarilycollaborative filtering models and perform extensive experi-used DNNs to model auxiliary information, such as acous-

ments on real-world dataset to show the eﬀectiveness of ap-

tic features of musics [3], images of items [13], and textual

plying deep learning to cross-domain recommender systems

information of items [14]. Diﬀerent from most traditional

for alleviating the user cold-start problem.

recommender systems within a single domain, in order to al-

The rest of this paper is organized as follows. We briefly

leviate the user cold-start problem, many recent works havereview related work on applying neural networks to recom-

focused on combining data from multiple domains, which ismender systems in Section 2. Then we describe the basic

called a cross-domain recommender system. One approach

information of the real-world dataset we used and our used

is to utilize information of a shared set of users between dif-method in Section 3. The experimental setup and results

ferent domains. For example, [15] proposed a method which

are presented in Section 4. Finally, the conclusion and fu-

improves performance of apps recommendation, news recom-

ture work are given in Section 5.

mendation and movie recommendation simultaneously based

2. Related Workon shared users’ search queries. The other approach canbuild a cross-domain recommender system without shared

There has been extensive study on applying deep learning

users or items between domains. In [16], the authors de-to recommender systems. In this section, we review a set ofveloped a generative model to find common clusters between

representative studies related to what we use in this paper.

diﬀerent domains and do recommendation. However, the ap-

The pioneer study on recommender systems, which usedproach is diﬃcult to scale well because of the computational

neural networks, proposed a two-layer Restricted Boltzmann

cost.

Machine (RBM) to model users’ explicit ratings on items [9].

Our work combines the advantages of utilizing deep learn-

Because of the strong performance of neural networks, deep

ing as CF technique and applying DNNs to extract users’ andlearning has become a popular choice for building a collab-

items’ features. We use information from multiple domains

orative recommender system [10, 11]. The user-based Au-

to build a cross-domain recommender system which allevi-

toRec [10] aims at learning a complex hidden structure toates the user cold-start problem for a real-world e-commercereconstruct a user’s rating given his or her historical rat-site. More specifically, to perform more accurate recommen-

ings of other items. In order to avoid learning an identity

dation for new users without interactions with the service.

function that always returns the same value as its argument,
which performs very poorly on unseen data by general au-

3. Method

toencoders, denoising autoencoders (DAEs) [11] have been

In this section, we would like to briefly describe the wholeused to learn from corrupted data, which means artificiallyprocess of our method first. We represent users by brows-

adding some noisy to original input data, in order to train

ing history in Ad platform without any users’ information

more powerful model which can generalize to unseen data.

in the online shopping domain, and represent items by their

Although these methods have showed the eﬀectiveness of

titles, sub-titles and descriptions through Word2vec. Then

neural networks for CF, they focused on explicit ratings only,

we train NeuMF based on bridge users of these two domains

which means they could fail to learn users’ preference based

and do recommendation to users who have little interaction

on positive-only implicit data, such as purchase history andwith the online shopping domain but with enough browsing

movie viewing activity, because the lack of negative feedback.

records in the Ad platform through ads distribution. Figure

For implicit feedback, there are also some recent works

1 illustrates the process of our work.

that applied deep learning to improve recommendation per-Then we would like to describe some basic information

formance [7, 12]. [12] proposed a collaborative denoising au-of the real-world dataset we used and existing solutions for

toencoder (CDAE) for CF with implicit feedback, which adds

handling implicit feedback. Then, we present our architec-

an additional user node to the input of the model comparedture to represent users and items in a latent low-dimensionalwith DAEs. [7] presented a state-of-the-art model called neu-

space. Lastly, we give details about the state-of-the-art neu-

ral matrix factorization (NeuMF) which combines a gener-ral collaborative filtering model NeuMF we used and how we

alized matrix factorization (GMF) model with a multi-layer

modified the model to fit our problem.

feed-forward neural network to model user-item interactions.

3. 1 Learning from implicit feedback

However, CF is generally unable to handle new users and new

We use a real-world online shopping dataset, an Ad plat-

items, which is known as the cold-start problem.
Handling the cold-start problem in recommender systems

form dataset and a shared users dataset between these twodomains. Online shopping dataset contains users’ purchaseFigure 2 Process for users and items representationline shopping domain to new users or people who have neverFigure 1 Process of our work

used the service before with abundant browsing history in Ad

records from 2017-05-11 to 2017-09-30 and products information such as title, textual description, category andprice. In Ad platform dataset, there is large amount of users’
browsing histories in the same period as the online shopping
dataset, when a user accesses a web page where the ads are
distributed, an access record will be stored in Ad platform.
The shared users dataset is an id mapping table of bridge
users between the online shopping domain and the Ad platform domain.
We build the cross-domain recommender system based onusers’ purchase records, which is a kind of implicit feedback
that can be defined as,1, if user u purchased item i;yui =0, otherwise.

platform, we decide to represent users only by corresponding browsing records in Ad platform but not information likeusers profile in online shopping domain, in order to generalize our recommender system to users in other domains. The
process for creating representation of users and items areshown in Figure 2.
As diﬀerent websites have diﬀerent structures and there areso many websites nowadays, it is unrealistic to run crawlers
for each website to collect contents, so we only extracted representative contents (title, keywords and description) fromweb pages in browsing records. In order to save time andreduce computational cost in collecting step, we collected
all the contents in the domain name level of URLs (e.g,https://www.kddi-research.jp/ ). However, this method has(1)

a problem. It will lose some important information which
can distinguish diﬀerent users more accurately. Although

It is important to note that a value of 1 for yui does notthis might further improve the performance, we leave it as amean user u actually likes item i, but indicates that an in-

future work. After getting a set of textual contents for eachteraction happened between u and i, or u has interest inbrowsing record, we use Mecab（注 1） to do the segmentation ofi. Similarly, yui = 0 does not necessarily mean the user uJapanese words, and only extract nouns, verbs, and adjec-

does not want to purchase this item, maybe just the user

tives to filter out meaningless words like stop words. Then

did not find the item before, which can be viewed as missing

we use a pre-trained Word2vec model to turn each word todata. According to this fact, implicit feedback contains noisy

its corresponding vector, and represent each browsing record

signals about users’ preference and naturally lacks negative

as the averaged vector. Similarly, for each user, we average

feedback.

vectors of his/her browsing records as his/her representation.

To handle the problem of recommendation with implicit

We adopt a similar process for item representation, i.e.,

feedback, many previous works formulated it as predicting

use textual features such as title, subtitle and description of

the scores of unobserved yui , which are used for ranking

each item and turn them to vectors by Word2vec.

the items [12, 17, 18, 19]. So the problem can be seen as

3. 3 Generalization of NeuMF

learning yˆui = f (u, i|θ) by optimizing an objective function,

Neural matrix factorization (NeuMF) is a state-of-the-where θ denotes parameters of predictive model and f de-art neural collaborative filtering model that ensembles lin-

notes an interaction function that represents the relation-

ear generalized matrix factorization (GMF) and non-linear

ship between users and items. For the objective function,

multi-layer perceptron (MLP). We would like to briefly intro-

we follow works [17, 18] to use point-wise loss, which aims

duce these two important components of NeuMF first, thenat minimizing the point-wise loss between yˆui and yui , andpresent generalized version of NeuMF.

sample negative instances from unobserved entries to deal

GMF. GMF is a model generalized from MF, which is thewith the absence of negative feedback [7, 17].
3. 2 Representation of Users and Items
Because our target is to recommend products in an on-（注 1）：http://taku910.github.io/mecab/most popular model for the recommendation field. In general, MF takes the one-hot encoding of user id and item id as
input, learns latent embedding vectors, and finally calculates
inner product of user’s and item’s latent vectors as the predicted score. The interaction function of MF can be definedasyui = aout (hT (pu ⊙ qi )),(2)

where pu denotes user latent vector, qi denotes item latentvector, ⊙ denotes the element-wise product of vectors. Also,
aout and h denote an identity function and a uniform vector
with all elements as 1, respectively. If we generalize aout toFigure 3 Structure of generalized NeuMF

other activation functions like sigmoid, and set h as weightsthat can be learnt from data, MF can be easily generalized toa more powerful and expressive version, which is the GMF.
MLP. MLP is a multi-layer feed-forward neural network,
which is used to learn interactions between users and items.
It is a more flexible model with a higher level of non-linearity
than GMF which can only model user-item interactions linearly from inner-product of two vectors. The MLP model
under [7] is defined as
[
z1 =

pu
qi

]
,

z2 = a2 (WT2 z1 + b2 ),......

(3)zL = aL (WTL zL−1 + bL ),ŷui = σ(hT · zL )where pu denotes a user latent vector, and qi denotes anitem latent vector as in GMF. Furthermore, Wx , bx and ax
denote a weight matrix, a bias vector and an activation function for the x -th layer of MLP, respectively. For ax , there
are many available choices such as sigmoid, tanh and ReLU.
As shown in [20], the sigmoid function is easy to suﬀer fromsaturation, which will cause vanishing gradient problem andmakes network stop learning. The tanh function can allevi-embeddings, which is more flexible and expressive than sharing the same embedding between these two components for
NeuMF. We adopt this setting and generalize NeuMF to fit
our problem by using representation of users and items mentioned in Section 3.2 as input, while [7] only uses one-hot
encoding of user (item) id as input. The interaction functionof NeuMF is defined as follows:G
zGM F = pGu ⊙ qi ,][pMu+ b2 )...)) + bL ),zM LP = aL (WTL (aL−1 (...a2 (WT2
qM
i
[
]
zGM F
T
ŷui = σ(h ·
),
(4)
zM LP
M
where pGu and pu denote user embedding for GMF and MLP,
M
respectively. Similarly, qG
i and qi denote item embedding

for these two parts, and ŷui is the predicted result of NeuMF.
We add a fully-connected layer after concatenation layer togive NeuMF ability to learn more complex user-item interactions. Then we apply batch normalization for each layer inorder to overcome the hard to converge problem which might
still be caused by ReLU and speed up training [25].
For all models mentioned above, the objective function isdefined as

ate the problems of sigmoid to some extent [21]. The ReLU
has been proven to be non-saturated, well-suited for sparsedata, and has higher convergence speed to speed up training. It can also reduce overfitting problem of models [22].

L=−∑

yui log ŷui + (1 − yui ) log(1 − ŷui ), (5)(u,i)∈Y∪Y −We select ReLU as the activation function in our work justwhere Y denotes the set of observed interactions and Y − de-like many other works [7, 23, 24].

notes the set of negative samples. This objective function is

NeuMF. Figure 3 shows the structure of generalized

known as logloss.

NeuMF in our work. NeuMF combines GMF and MLP in
their last hidden layers for better modeling user-item inter-

4. Experimentactions by enabling them to mutually reinforce each other.

In this section, we conduct experiments to evaluate theAs the authors in [7] allow GMF and MLP to learn separate

performance of NeuMF for alleviating the user cold-startTable 1 Statistics of the training set
Dataset#interactions #users #items Sparsity(%)

Training

156,287

14,65918,51199.942

Table 2 Statistics of the validation set and test set
Dataset#interactions #users #items
Validation

12,410

9,937

1,539

Test

14,443

10,273

2,369

problem. Then we compare NeuMF with several baseline
recommendation algorithms to demonstrate the eﬀectiveness

Figure 4 Performance of HR@Kof deep cross-domain recommender systems.
We first introduce the setting of real-world dataset we used

set K as integers from 1 to 10 for evaluation, calculate bothfor training and evaluation. Then we present evaluation cri-

metrics for each interaction and show the average score.

teria, briefly review baseline methods and implementation

Baselines. We compare the NeuMF that we used in our

details about all methods that have been used in the experi-

work with diﬀerent kinds of methods:

ment. Finally, we show the recommendation performance of
diﬀerent methods and discuss the results.
4. 1 Experimental Settings- Cosine Similarity. This is the standard method tomodel user-item relationship, which is widely adopted as the
final step in recommendation models.

Dataset. As mentioned in Section 3.1, we have an ad plat-- GMF [7]. As mentioned in Section 3.3, GMF is a gen-form dataset with users’ browsing histories and an online

eralized version of traditional MF. It is a shallow model thatshopping dataset with users’ purchase records from 2017-05-

learns user-item interactions from inner-product of input vec-

11 to 2017-09-30. We divide purchase records into threetors, which is more expressive than traditional MF.

parts: 2017-05-11 to 2017-09-10 for training, 2017-09-11- DMF [24]. DMF is a state-of-the-art deep matrix fac-

to 2017-09-17 for validation, 2017-09-18 to 2017-09-30 for

torization model that projects users and items into a low-

testing, and only use browsing histories from 2017-05-11 todimensional latent space by DNN, and calculates cosine sim-

2017-09-10 for user modeling to avoid using future informa-

ilarity of these two vectors as predicted result. We follow thetion to predict the past. As the original data is highly sparse,

result in [24] to build a DMF with 2 layers and tune other

we retained only users with at least 5 interactions (purchases)

hyper-parameters to report the best performance.

as a training set. The statistics are summarized in Table 1.

Implementation Details. All methods are implemented

In order to evaluate recommendation performance on new

based on PyTorch（注 2）. For all models except Cosine Simi-

users, we filtered all the purchase records of users in training

larity, we randomly sampled four negative samples for each

set out of validation and test sets. The statistics of these two

positive instance. Parameters to be trained are randomly

datasets are shown in Table 2.

initialized from Gaussian distribution N (0, 0.012 ). The opti-

Evaluation Criteria. To evaluate the performance ofmizer is SGD with batch size of 256, and we tested learning

item recommendation, we follow the common strategy ofrate of [0.001, 0.005, 0.01]. We also tested the number of

top-K recommendation in [7, 23] to rank a target item ofneurons in the last hidden layer of [16,32,64,100]. As foreach interaction with items that are not interacted by the

structure of neural network models, we used a tower pat-user. Because the items in our online shopping domain will

tern, which halves the layer size for each successive higher

only exist during a period of time, we found there are onlayer. For determining the best hyper-parameters for each

average about 1,500 items for sale each day from 2017-05-

model, we tuned hyper-parameters based on the validation

11 to 2017-09-10. Due to this fact, we randomly sample

set.

1499 items having no interaction with the user, and rank the

4. 2 Experimental Results

1500 items, which is diﬀerent from [7, 23] that only sampled

In this section, we report the recommendation performance

99 items with no interaction for ranking. The performanceof the methods and discuss the results. Figure 4 and Table 3of a ranked list can be evaluated by Hit Ratio (HR) andshow the performance of HR@K with K varied from 1 to 10.

Normalized Discounted Cumulative Gain (NDCG) [26]. HR

Figure 5 and Table 4 show the performance of NDCG@K in

intuitively measures whether the target item is presented on

the same situation.

the top-K list, while NDCG accounts for hit position by as-

We can see that deep learning based models NeuMF andsigning higher scores to hits at top ranks. In our work, we（注 2）：https://pytorch.org

Table 3 Performance of HR@K(%)
K

1

2

3

4

NeuMF

0.727

2.278

5.172

9.229

5

6

7

8

9

10

13.771 17.725 20.951 23.167 24.815 26.165

DMF

2.278

5.11

7.699 10.088

12.39414.70616.76918.69420.42521.817

GMF

1.052

2.804

4.078

5.144

6.003

7.125

8.032

9.036

10.00510.898Cos-Similarity

0.187

0.346

0.485

0.623

0.727

0.872

0.99

1.156

1.316

1.475

K

1

2

7

8

9

10

NeuMF

0.00730.0171

Table 4 Performance of NDCG@K
3456
0.03150.049

0.06660.0807 0.0914 0.0984 0.1034 0.1073

DMF

0.0228 0.0406 0.0536 0.0639 0.0728

0.081

0.08790.094

0.09920.1032

GMF

0.01050.02160.02790.03250.03590.03990.0429

0.046

0.049

0.0515Cos-Similarity

0.00190.00290.00360.00420.00460.00510.00550.006

0.00650.0069of these two domains. Then we applied diﬀerent kinds ofcollaborative filtering models to build cross-domain recommender systems and evaluated their recommendation performance with respect to the new users of online shopping
domain. The experimental result shows that deep learning
based models far outperform other shallow models for collaborative filtering for alleviating the user cold-start problem.
Moreover, NeuMF can model overall user-item interactions
better than DMF in HR@K when K is between 5 and 10,
Figure 5 Performance of NDCG@K

which shows the model combines linearity and non-linearity
is more powerful than the pure non-linear model in our task.

DMF consistently give better performance in both metrics
than other existing methods for all the values of K, with improvement up to 15.267% in HR@10 compared to the best
shallow model GMF. For NDCG@10, the best deep learning
based model NeuMF is about 2.08 times as that of GMF.
It shows the eﬀectiveness of neural approaches in alleviating user cold-start problem with knowledge introduced fromother domain.
We can also find that DMF achieves the best performance
than other methods in HR@K for K from 1 to 4. But when
K is larger than 4, NeuMF outperforms DMF and achieves
4.348% improvement when K = 10. It shows that DMF isbetter at ranking a part of target items in higher positions
than NeuMF does. While NeuMF is better at modeling the

In future, we will study diﬀerent kinds of user modelingmethods, such as applying TF-IDF [27] to give diﬀerent
weights to diﬀerent URLs and words, or directly turn URLs
to vectors to represent users [28], to further improve the result of NeuMF. Also, we plan to further explore the impactof time to the recommendation performance and how thesemethods perform respect to new items in online shopping
domain. Moreover, we are particularly interested in building
cross-domain recommender systems through transfer learning methods like domain adaptation [29].

Acknowledgment
This research is partially supported by JST CREST Grant
Number J181401085.

overall user-item interactions and can rank more target items
into the top-10 ranked list than DMF, which is more practical because it is general to recommend a list of items tousers. The results of NDCG@K also show this fact, as DMF
still gives better performance than NeuMF in NDCG@5 and
NDCG@6, while it has already underperformed NeuMF in
HR@5 and HR@6.

5. Conclusion and Future WorkIn this work, we transferred information from an Ad platform to an online shopping domain based on bridge users

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