DATA PAPER - Am MIT spezialisierte KI-Forschung

DATA PAPER

User Profiling for CSDN: Keyphrase Extraction,
User Tagging and User Growth Value Prediction

First-place Entry for User Profiling Technology Evaluation
Campaign in SMP Cup 2017

Guoliang Xing1,2, Hao Gao1,2, Qi Cao1,2, Xinyu Yue1,2, Bingbing Xu1,2, Keting Cen1,2 & Huawei Shen1,2†

1Key Laboratory of Network Data Science and Technology, Institute of Computing Technology, Chinesische Akademie der Wissenschaft,

Peking 100190, China

2University of Chinese Academy of Sciences, Peking 100049, China

Schlüsselwörter: User profiling; Keyphrase extraction; User tagging; Growth value prediction; Word embedding

Zitat: G. Xing, H. Gao, Q. Cao, X. Yue, B. Xu, K. Cen & H. Shen. User profiling for CSDN: Keyphrase extraction, user tagging

and user growth value prediction. Datenintelligenz 1(2019), 137-159. doi: 10.1162/dint_a_00015

Erhalten: August 27, 2018; Überarbeitet: November 29, 2018; Akzeptiert: Dezember 6, 2018

ABSTRAKT

The Chinese Software Developer Network (CSDN) is one of the largest information technology communities
and service platforms in China. This paper describes the user profiling for CSDN, an evaluation track of SMP
Cup 2017. It contains three tasks: (1) user document keyphrase extraction, (2) user tagging and (3) user
growth value prediction. In the first task, we treat keyphrase extraction as a classification problem and train
a Gradient-Boosting-Decision-Tree model with comprehensive features. In the second task, to deal with class
imbalance and capture the interdependency between classes, we propose a two-stage framework: (1) für
each class, we train a binary classifier to model each class against all of the other classes independently; (2)
we feed the output of the trained classifiers into a softmax classifier, tagging each user with multiple labels.
In the third task, we propose a comprehensive architecture to predict user growth value. Our contributions
in this paper are summarized as follows: (1) we extract various types of features to identify the key factors in
user value growth; (2) we use the semi-supervised method and the stacking technique to extend labeled data
sets and increase the generality of the trained model, resulting in an impressive performance in our
experiments. In the competition, we achieved the first place out of 329 teams.

† Corresponding author: Huawei Shen (Email: shenhuawei@ict.ac.cn; ORCID: 0000-0002-1081-8119).

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User Proﬁ ling for CSDN: Keyphrase Extraction, User Tagging and User Growth Value Prediction

1. EINFÜHRUNG

User profiling is very important for precise recommendation and personalized services in the Internet
era. In diesem Papier, we focus on three key tasks of user profiling for the Chinese Software Developer Network
(CSDN), an evaluation track of SMP Cup 2017 [1]. CSDN is one of the largest information technology
communities and service platforms in China. CSDN has about 50 million registered users and over 100,000
users communicate and share knowledge and information via its Web forums every day [1]. The three tasks
of user profiling for CSDN include user document keypharase extraction, user tagging and user growth
value prediction.

Keyphrase extraction is to automatically extract the top k important phrases that can represent the main
idea of a document. A supervised method is used for keyphrase extraction. Erste, we generate 301,076
candidate phrases for the whole corpus. It covers more than 90% over the annotated keyphrases in the
training set. Then we extract statistical features and semantic features of the input examples, and compare
the effectiveness of these features. Endlich, by incorporating both the statistical features and semantic
Merkmale, we achieved the highest score among all competitors.

The second task is to label user interests. The challenge contains a variety of issues. Erste, users may have
more than one interest. The problem can be solved by creating several independent binary classifications.
Jedoch, the correlations between interests are not considered. Zum Beispiel, users labeled with “machine
learning” are likely to have the interests of “deep learning.” Second, the labeled data are insufficient due
to costly manual labeling. If there are too many parameters in the model, it may lead to overfitting. To
address the above-mentioned challenges, we propose a two-phase model. To encode the high dimensional
features into low dimensional features to decrease the number of parameters, the independent binary
classifications are formulated and the probabilities of user labels are predicted by each classifier in the first
Phase. To capture the correlations between labels, the neural network with softmax loss is adopted, Wo
the probabilities from the first phase are regarded as the input in the second phase. The labels corresponding
to the highest probabilities predicted by the neural network are users’ final labels.

In task 3, we need to predict the growth value of users in CSDN during the next year based on their
posts and behaviors. After formulating this task as a regression problem, we explore various types of features
to incorporate the key factors driving the growth of user value. Besides, semi-regression methods and a
stacking technique are introduced into our prediction architecture to overcome the lack of labeled data
and enhance the generality of the prediction model. Experimental results show that our proposed architecture
can effectively predict user growth value.

The rest part of this paper will introduce our work in detail for each task, and the last section presents

the conclusions and the future work.

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User Proﬁ ling for CSDN: Keyphrase Extraction, User Tagging and User Growth Value Prediction

2. KEYPHRASE EXTRACTION

Keyphrases are single or multi-word expressions that can represent the main topics of a document. Sie
are proved useful in many areas, z.B., information retrieval [2], text summarization [3], text clustering [4],
text categorization [5], text indexing [6] and opinion mining [7]. Darüber hinaus, keyphrases help users grasp
the main topics of a Web page [8].

2.1 Related Work

Keyphrase extraction methods can be divided into two categories, unsupervised and supervised methods.
In both of them, the extraction process can be divided into two steps: candidate phrase generation and
finding top k phrases.

Candidate phrase generation is to extract a set of phrases or words using heuristic rules. These rules
sollte sein (1) allgemein: the rule should not depend on the training data only; (2) high quality: the generated
candidate phrase set should include the keyphrases that are “labels” in training data as many as possible;
(3) minimum size: rules are designed to avoid spurious instances and keep the number of candidates to a
minimum. Typical heuristic rules include removing stop words [9], allowing n-grams that appear in
Wikipedia article titles to be candidates [10], extracting noun phrases [11], extracting n-gram or noun
phrases that satisfy pre-defined lexico-syntactic patterns [12]. In diesem Papier, we generate candidate phrases
with a frequency threshold rule.

Extracting top k keyphrases is to find the most important phrases from a document. The typical unsupervised
methods of this approach can be categorized into ranking-based methods such as TF-IDF and TextRank [13]
and clustering-based methods. TF-IDF, short for term frequency–inverse document frequency, is a numerical
statistic that can reflect how important a phrase is to a document in a collection or corpus. The TextRank
algorithm is an extension of the PageRank algorithm, a graph-based ranking method which was initially
used to rank the importance of Web pages [14]. The idea of TextRank is that the importance of a candidate
in a certain document depends on its relation with other candidate phrases in this document, which means,
the more other candidate phrases a candidate is related with, the more important this candidate is; Die
more important other candidates are related to a candidate, the more important this candidate is. Researchers
computed relatedness between candidates using co-occurrence counts [15] and semantic relatedness [16],
and used TextRank to generate top k important keyphrases [13].

The two important issues of the supervised method for keyphrase extraction are feature design and
modeling. Berend and Farkas [17] invented SZTERGAK system to extract features. They classified features
to five categories, i.e. standard features, phrase-level features, document-level features, corpus-level features
and external knowledge-based features. These features perform well on their tasks, but there are still some
other features to discover. Zum Beispiel, the semantic embedding features for the phrases and documents.

Keyphrase extraction is treated as a binary classification task. For the supervised method, the goal of
modeling is to train a classifier to determine whether a candidate phrase is a keyphrase, while the annotated

Datenintelligenz

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User Proﬁ ling for CSDN: Keyphrase Extraction, User Tagging and User Growth Value Prediction

keyphrases in a document and other non-keyphrase candidates are used to generate positive and negative
examples. So far, researchers have tried different learning algorithms to train the binary classifier. Frank
et al. used naïve Bayes to train the classifier [18], and Turney tried using decision trees in 1999 [19]. Some
other learning methods such as maximum entropy [20], multi-layer perceptron [21] and support vector
machines [22] are also explored. Gradient boosting is a machine learning technique used for regression
and classification problems, and eXtreme Gradient Boosting (XGBoost) is an optimized distributed gradient
boosting library designed to be highly efficient, flexible and portable [23]. In the keyphrase extraction task,
we choose XGBoost tool, and the inputs are features of pair, where the doc represents one
document, and the phrase is a candidate phrase extracted from this document.

2.2 Candidate Phrase Generation

In keyphrase extraction task of SMP Cup 2017, the organizers provided a total of 1,022 documents
together with five annotated keyphrases for each document. These annotated keyphrases were used for
positive examples, and the other candidate phrases were taken as negative examples.

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2.2.1 Verfahren

Existing methods for candidate phrase generation, as introduced in Section 2.1, work well for English
documents, but are not suitable for Chinese documents due to challenges in Chinese word segmentation.
Darüber hinaus, the formats of the annotated keyphrases in training document set are quite diverse. Speziell,
they can be a single Chinese character, a Chinese phrase with multiple characters (mostly with 2 Zu 7
Figuren), an English word, an English phrase with multiple words, a phrase consisting of both English
and Chinese, and even an English phrase containing special characters, like c++, node.js, utf-8, usw. Der
high diversity of keyphrase format greatly increases the difficulty of candidate phrase generation.

To combat the aforementioned challenges, we adopt a heuristic procedure to automatically generate the

candidate phrases (Figur 1):

1). Take the total 1 million documents as a corpus.
2). Extract the Chinese content of each document in the corpus.
3). Use seg-for-search-engine mode in Jieba to obtain the segmentation results of the Chinese phrases.
4). Extract Chinese phrases with 2 Zu 7 Figuren.
5). Use phrase frequency to filter phrases extracted in step 4.
6). T raverse the whole corpus and extract English phrases or combination phrases, which consist of both
English words and Chinese characters generated in step 5, and order them by frequency to generate
non-Chinese format candidates.

7). Combine the candidate phrases extracted in steps 5 Und 6.

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User Proﬁ ling for CSDN: Keyphrase Extraction, User Tagging and User Growth Value Prediction

Corpus

English and
combination phrases

Chinese part

Chinese
candidates

Candidate
phrases

Word segmentation
results

Length between
2 Und 7 Figuren

Figur 1. Candidate phrase generation procedure.

Since a higher proportion of keyphrases in training set and the documents in this corpus are Chinese,
we deal with Chinese phrases and English phrases separately in step 2. For Chinese phrases, the difficulty
is how to balance the number of candidate phrases and the coverage of annotated keyphrases in the training
set. At the very beginning, we simply use an open source library named Jieba for word segmentation. Der
shortcoming of this method is that only a small proportion of annotated keyphrases are included in the
candidate phrases. Then we extracted every possible phrase with a length between 2 Und 7 Figuren,
which leads to explosive growing number of candidate phrases. There is a heuristic rule that the minim um
unit of a Chinese phrase is a word instead of a character, so finally we use seg-for-search-engine mode in
Jieba to extract every possible word and its position in the document. Then we construct every possible
phrase with a length between 2 Und 7 Figuren (step 3 and step 4). In step 5, we calculate the phrase
frequency in the whole corpus, and use phrases with high frequency as the Chinese candidate phrases.
Dann, we traverse the corpus again to extract the phrases only in English and combination phrases. In the
final stage, we merge all candidate phrases generated in step 5 and step 6 as our final candidate phrases.

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2.2.2 Ergebnisse

In total, we extract 301,076 phrases as the candidate phrases. Note the phrases that include special
characters are not included, for there are a great number of such phrases in the whole corpus. As the amount
of documents we need to deal is small, we extract phrases with special characters for each document.
Endlich, we generate 239,405 training examples from 1,022 documents in the training set and 233,580
validation examples from 1,022 documents in the validation set. We analyze the relation between candidate
phrases in training examples and validation examples. The results are shown in Table 1. Our candidate
phrases cover more than 90% of the annotated keyphrases. The difference between row 1 and row 2 comes
from the noise in data, as there are some annotated keyphrases which do not exist in that document. Der
examples in “not in candidate phrases” of row 2 and row 3 are those phrases with special characters.

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User Proﬁ ling for CSDN: Keyphrase Extraction, User Tagging and User Growth Value Prediction

Tisch 1. Candidate phrases.

In candidate phrases

Not in candidate phrases

4,711
4,454
229,986
233,580

399
14
4,951
4,983

Annotated keyphrases
Positive training examples
Negative training examples
Validation examples

2.3 Feature Engineering

2.3.1 Statistical Features

Similar to Berend and Farkas’ work [17], we divide the statistical features into four categories, Merkmale
at the phrase level, at the document level and at the corpus level and external knowledge-based features.

Phrase-level features: In this task, we generate 31-dimensional features at the phrase level in terms of
phrase length and formats: Chinese or English phrases, phrases containing special characters, the location
of the special characters, a noun or a phrase containing a noun, the number of Chinese characters, letters
or special characters it has, a subset of other candidate phrases (if phrase A is a subset of phrase B, Dann
we call B is a super-phrase of A), usw.

Document-level features: They are calculated based on the candidate phrases and the document the
phrases belong to. The intuitive features are calculated directly between a phrase and the document, wie
phrase frequency, whether a phrase appears in the title, and the location features. Auch, the features of the
document and the sentence in which a candidate phrase appears are also included in our model. Ein anderer
type of important document-level features is the relation between a candidate phrase and some generated
results from a document. Zum Beispiel, we analyze top 10 keywords, top 10 keyphrases and top 3 key
sentences of a document using TextRank, and calculate whether a candidate phrase is included in the
results. Endlich, we calculate 27-dimensional document-level features in total.

Corpus-level features: We extract totally 4-dimensional features. They are phrase frequency in the whole

corpus, the TF-IDF value, IDF value, and the total number of titles in which a candidate phrase appears.

External knowledge-based features: We use two external knowledge sources, the Wikipedia entry
data and the Baidu Encyclopedia entry data. Speziell, we extract 4-dimensional features: whether a
candidate phrase is a Wikipedia entry or is a Baidu Encyclopedia entry, whether a candidate phrase has a
super-phrase that is a Wikipedia entry or a Baidu Encyclopedia entry.

2.3.2 Semantic Features

Semantic features refer to the word embedding features. In this task, the semantic features we extract
include the embedding vectors of each candidate phrase, statistical features of the embedding vector and
some distance features between a candidate phrase and the document it belongs to. The main procedures
are summarized as follows.

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User Proﬁ ling for CSDN: Keyphrase Extraction, User Tagging and User Growth Value Prediction

1). Generate the embedding vector for every word in the corpus. There are two frequently used
open source libraries, the word2vec and the GloVe. While the GloVe focuses on the global
Information, the word2vec focuses on the local information. The dimension is also an important
hyper-parameter in word embedding. In this task, we set the dimension to be 128 Und 256. Endlich,
we generate four types of embedding vectors for each word in the corpus.

2). Generate the embedding vector for phrases and document. We represent the embedding
vectors of a candidate phrase or a document simply by summing the embedded values of words it
is composed of. Note that the embedding vectors for candidate phrases and a document could be
normalized.

3). Calculate the distance features and the distribution features. Since a document’s title often
contains its main idea, we use the embedding vectors of the title instead of the document to calculate
the distance features of a candidate phrase, d.h., the distance between the title of the document and
the candidate phrase. There are eight types of distance features we generate, d.h., the cosine similarity,
the Cityblock distance, the Jaccard coefficient, the Canberra distance, the Euclidean distance, Die
Minkowski distance, the Bray-Curtis distance and the word mover’s distance [24]. The distribution
features can reflect the distributions of the embedding vectors of a candidate phrase in a title. Wir
calculate the skewness and the kurtosis value as the distribution features.

2.4 Experimental Results

We finally extract 66-dimensional statistical features and 400-dimensional semantic features. We compare
the effect of both the statistical features and the semantic features by using XGBoost. The parameters in the
model are listed as follows: eta = 0.03, max_depth = 8, subsample = 0.8, colsample_bytree = 1.0, alpha
= 0, lambda = 1 and gamma = 0. The performances on the validation set for different types of features are
illustrated in Table 2.

Tisch 2. Performance of different types of features.

Features

Semantic features
Statistical features
Beide

Score

0.533
0.631
0.676

Tisch 2 shows that the statistical features perform better than the semantic features. When combining
the two types of features, we find that the performance is significantly enhanced, which means that the
semantic features contain more information than the statistical features. Endlich, we use the combined
features as our model input, and our score is the best both in the validation set and in the test set.

3. USER TAGGING

It is challenging to label users’ interests in our data set due to two main reasons: (1) users may have
several interests, Und (2) the labeled users are insufficient. To tackle these challenges, we propose a two-
phase model to assign labels to users.

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User Proﬁ ling for CSDN: Keyphrase Extraction, User Tagging and User Growth Value Prediction

3.1 Related Work

Boutell et al. [25] proposed binary relevance which independently trains one binary classifier for each
label. For unseen instance x, the labels that x has are those that their probabilities are beyond the threshold.
Jedoch, the correlations between labels are not considered in the model. Read et al. [26] leveraged the
correlations and proposed a classier chains model. The basic idea is to form a chain of binary classifiers,
where the prior result will be the feature in the next model. Bedauerlicherweise, when the cardinality of label
space is large, this chain method is not scalable. Another heuristic way is to learn the neural network with
softmax loss, and the labels with the top r highest probabilities are assigned to users. Jedoch, when the
training set is small, it is likely that the model is overfitting with high-dimensional features.

3.2 Problem Formulation

Let Dtrain = {(U1, L1), (U2, L2), … , (UND, LN)}

denote the training set containing |Dtrain| = N users,
and Dtest = {(U1, L1), (U2, L2), … , (UM, LM)}
denote the test set containing |Dtest| = M users, Wo
Ui = (Ui1, Ui2, … , Uid) deno tes ds features of user Ui, and Li = (Li1, Li2, … , Lir) denotes r labels for each user.
Let LS = {ls1, ls2, … , lsk} denote label space which contains k labels and for (Ui, Li), Li ⊆ LS. Tisch 3
summarizes the notations used in this paper. For simplicity, we denote both Dtrain and Dtest as D. Given this,
we define the input and output of our problems as follows:

Input: the input of our problem consists of a set of N users in Dtrain with corresponding r labels.

Output: Assign r labels from LS to M user in Dtest.

Tisch 3. Notations.

Symbol

Description

Dtrain
Dtest
N, M
Ui = (Ui1, Ui2, … , Uid)
Li = (Li1, Li2, … , Lir)
LS = {ls1, ls2, … , lsk}

3.3 Two-phase Model

The training set
The test set
Der # of users in training set and test set
The d dimensions of user Ui
The r labels that Ui has
The label space that contains k labels

In Section 3.2, we formulate the problem of labeling users as a multi-label classification problem, Wo
our goal is to tag each user with labels from given label space. Compared with the traditional multi-label
classification problem, where the number of labels assigned to each sample is not necessarily the same,
our task requires us to assign the same number of labels to each user. In diesem Abschnitt, we take into account
both insufficient samples and the correlation among labels.

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User Proﬁ ling for CSDN: Keyphrase Extraction, User Tagging and User Growth Value Prediction

Figur 2 illustrates the framework of our two-phase model, where the first phase is concerned with
independent binary classifications (blue rectangle) and the second phase a neural network (green rectangle).
In the first phase, we formulate each label as a binary classification problem. After training, we obtain k
classifiers. In the second phase, we train a neural network, where the input values are the probabilities
predicted by the first phase, and the softmax is the loss function. Endlich, r labels with the highest probabilities
are chosen to be a user’s interests.

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Figur 2. The framework of a two-phase model.

3.3.1 Independent Binary Classifications

We first transfer the training set and test set as follows: for lsi in label space LS, if lsi ∈ Lj for (Uj, Lj) In
D, {(Uj,1)} is a training sample for label lsi, ansonsten {(Uj,0)} is a training sample for label lsi. Denoting with
ˆ
iD the set of training samples for label lsi, we have

ˆ
D
ich

{(

w
U ls L
(

ich

))}

w
(

ls L
,
ich

)

⎧
1,
= ⎨
0,
⎩

ls
ich
ls
ich

∈
∉

L
L

(1)

(2)

Then we train k binary classifications with ˆ

iD independently. Zusätzlich, we leverage bagging strategy

for learning from ˆ

iD to reduce the generalization errors.

Bagging strategy We divide ˆ

iD into c folds randomly, and the continuous c-1 folds are for training, Die

ausruhen 1 fold for validation. Endlich, we obtain c classifiers for ˆ

iD .

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After training, k * c classifiers are learned, where k is the number of labels in label space, and c is the
quantity of classifiers of each label. When predicting unlabeled users in Dtest, the features of users are fed
to c classifiers for each label in label space and the mean of the probabilities predicted by c classifiers is
used as the input which is fed to the second phase.

3.3.2 Softmax Classification

The labels which correspond to the highest r probabilities predicted by the independent binary
classifications can be assigned to users heuristically. Jedoch, there are two main weaknesses. Erste, Die
probabilities of different labels are not comparable, z.B., the label with higher probability may not be the
users’ label as the probabilities are not predicted by the same model. Zweite, the relevance of labels is not
berücksichtigt, which is a strong prior that the first phase does not leverage.

Here we introduce the model of the second phase, which is a neural network with softmax loss to resolve
the above weaknesses. Figur 3 illustrates the three-layer architecture of the neural network of our model.
The input layer contains r units having r probabilities predicted by the independent binary classifiers, Und
the output layer contains r units. In the hidden layer, we leverage dropout to avoid overfitting. The softmax
loss is defined as below:

loss

1
= ∑
N

f P L
(
,
ich
ich

(3)

where Pi is the probability of user i’s labels predicted by the previous classification, and Li is the ground
truth of user i, Und

(
f P L
ich
ich

)

= −∑

L logP
i j
i j
1

(4)

Ähnlich, we adopt bagging strategy to decrease the loss in generalization performance. When predicting

the unlabeled users, the labels corresponding to the highest probabilities are the users’ labels.

3
3

…

Input Layer

Hidden Layer

Output Layer

Figur 3. The architecture of the neural network.

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3.4 Experiments Results

3.4.1 Data Collection

The problem is that given the blog content, the users’ behaviors, the structure relationships and chatting
relationships between users in CSDN, we need to tag each user with three labels from the label space of
42 labels, z.B., machine learning, Web development, usw. Tisch 4 lists the statistics of the data collection.

Tisch 4. The statistics of data sets.

Item

#blogs
#record of posting
#record of browsing
#record of commenting
#record of voting up
#record of voting down
#record of favorite
#structure relationship
#chatting relationship
#users in training set
#users in test set

Volumen

1,000,000
1,000,000
3,536,444
182,273
95,668
9,326
104,723
667,037
46,572
1,055
1,055

Notiz: “Voting up” means users click a “like” button, while “voting-down” means users click a “dislike” button.

3.4.2 Feature Definition

It turns out that content features are the most helpful features, of which the basic idea is that the users
with similar interests tend to post (browse, comment on, add favorites, vote down and vote up) the similar
blogs.

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Doc2Vec [27]: Doc2vec is an unsupervised algorithm that learns fixed-length feature representations
from variable-length pieces of texts, and the docs are embedded into distributed vectors. We believe the
users associated with similar blogs tend to have the similar interests. The features of users are defined as
the representations of blogs in six types of behaviors (browse, post, comment, add favorites, vote down and
vote up).

In our experiments, blogs are represented by the vector of 200 dimensions. Endlich, the users are

represented by vectors of 1,200 dimensions (200 dimensions – six types of behaviors).

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3.4.3 Evaluation Metrics and Baselines

To evaluate our proposed model, the following performance metric is considered:

Punktzahl

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M =
ich

L
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∩

*
L

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L
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(5)

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* is the ground truth. For each user,
where M is the cardinality of the data set, Lii is the predicted labels and Li
the score is 1, 2/3 oder 1/3, wenn die 3, 2, oder 1 label(S) are predicted correctly. The higher the score, the better
performance we achieve.

We compare the following methods for user labeling:

Top r labels (TL): This method tags each user with the most r popular labels in the training set. In our

data set, r = 3.

Independent binary classifications (IBC): This is the first phase in our model, which tags the unlabeled
users with the labels corresponding to the highest r probabilities predicted by independent binary
classifications.

Softmax classification (SC): This is the second phase in our model, where the inputs are the features
defined in Section 3.4.2 instead of probabilities, and the neural network with softmax loss is leveraged.
Endlich, the top r labels are assigned to each user.

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3.4.4 Performance Analysis

We perform the experiments on the data set described in Section 3.4.1, and the results are based on the

test set with 1,055 Benutzer.

Tisch 5 shows the proposed method achieves better performance than other methods. Since IBC does
not consider the correlations between labels, the probabilities predicted by the independent classifiers are
not comparable, while the correlations are captured in the second phase of our model. The parameters in
SC are far more than the number of training set, causing overfittings of the training data.

Tisch 5. Comparison of scores of methods.

Method

Score

0.3754

IBC

0.45592

0.4436

Our method

0.46319

4. USER GROWTH VALUE PREDICTION

In this task, we need to predict the growth value of users for the next year based on their posts and
behaviors in the past year. The growth value for each user has been regularized in [0,1], Wo 0 represents
the loss of a user.

4.1 Architecture

The task of user growth value prediction can be easily formalized as a typical regression task in machine
learning, d.h., given that feature xi contains the statistics of behaviors and posts of user i, predict his/her

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future growth value yi. The whole architecture to solve this task is shown in Figure 4, which consists of
features layer, primary model and model ensemble.

Modell
Ensemble

Primary
Modell

Features

Stacking

Semi-Supervised
Regression

MLP

SVR

XGBoost

Random Forest

Statistics of user actions

Statistic of documents

Representation of user-document

Representation of user-user

Figur 4. Architecture of user growth value prediction.

4.2 Features

4.2.1 Statistics of User Behaviors

Frequency of behavior: We count the number of behaviors triggered by a user during a certain period,
d.h., the number of posts the user published, viewed, commented on, collected, voted up and voted down,
the number of private messages the user received and the number of private messages the user sent to
Andere. This type of features reflect how active a user is during a certain time, z.B., one quarter, which has
also been used in customer lifetime value prediction [28].

Change of behavior frequency: We calculate the change of behavior frequency. In particular, Wir
subtract the frequency of posts by the user during the last two quarters from the frequency of posts by the
user during the last one quarter.

The last active time: We find the last time of a certain behavior of a user, such as writing posts,

commenting on posts, viewing posts and so on. These features also reflect the activities of the user.

The proportion of a certain type of behavior: We calculate the proportion of a certain type of
behavior over all behaviors during a certain period, d.h., one quarter. These features characterize the
preference of a user, z.B., prefer writing articles to browsing others’ articles.

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User Proﬁ ling for CSDN: Keyphrase Extraction, User Tagging and User Growth Value Prediction

Number of retweeted posts: We notice that the posts created by a user and the posts retweeted by
others have quite different influences, and thus should be treated differently when studying user activities
and their posts’ quality. We distinguish the original posts and retweeted posts based on the MinHash
Algorithmus [29]. The main assumption is that if we have two similar posts, we regard the oldest one as the
original post and the others as retweeted posts. Speziell, we calculate a 64-byte hash value for each
post. It would be costly if we consider all pairs of posts. Folglich, we use the first 16-byte of hash
value as key. We only compare the hash value when the keys of two posts are the same. We count the
number of original posts published by the user during one quarter as a feature. This feature can reflect the
quality and creativity of the user to a certain extent.

User points in the past year: We calculate the point of a user in the past year according to the CSDN

user point rule published online. This feature can also reflect the quality of the user.

4.2.2 Statistics of Documents

Length of documents: We calculate the average length and variance of documents written by a user.

These features reflect the characteristics of documents written by the user.

Feedback received by documents: We count the number of a user’s behaviors, such as browsing,
commenting, voting up or voting down and feedback the user received for his or her posts during a certain
Zeitraum, z.B., one quarter. This type of features can reflect the quality of documents written by the user.

4.2.3 Representation of User-document

We also want to extract some useful information from the user-document interaction matrix. We construct
six types of interaction matrices, d.h., user-document browse matrix, user-document comment matrix, user-
document vote-up matrix, user-document vote-down matrix, use-document collection matrix, user-document
all behaviors matrix. The element in a matrix represents the frequency of a user’s certain behavior during
the past year. We decompose these matrices with non-negative matrix factorization (NMF) [30] and use the
user representation as features. These features reflect the similarity of user behaviors in a transformed space.

4.2.4 Representation of User-user

Matrix factorization Similar to representation of user-document, we construct two types of user-user
matrices, d.h., matrix of following relations and matrix of sending private messages. We also use NMF to
decompose these two matrices (d.h., each row corresponds to a user vector) and use the user vectors as
Merkmale.

PageRank We construct a user-user interaction network to reflect the influence between users. If user
A browses an article written by user B, then there exists an edge likely from A to B. We run the PageRank
Algorithmus [14] on this network to calculate the importance of each user.

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User Proﬁ ling for CSDN: Keyphrase Extraction, User Tagging and User Growth Value Prediction

4.3 Primary Model

We adopt four primary models with significant difference, d.h., Multi-layer Perception (MLP), Support
Vector Regression (SVR), XGBoost and Random Forest to predict user growth value. For each primary
Modell, we employ the sampling strategy to keep a model’s generality (Figur 5). Speziell, after sampling
k features for K times and sampling n samples for N times, we obtain N * K different training data sets for
each primary model, resulting in N * K different trained models. When performing the final prediction, Wir
combine all the N * K results before bagging them with the median value. Nächste, we give a simple instruction
of each primary model and their performance, where the evaluation score is defined in Equation (6):

Punktzahl

= −
1

1
N

∑

ich

⎧
⎪
⎨
⎪⎩

ich

−

ich

max

(
v v
,
ich

ich

)

ich

ansonsten

(6)

where vi is the true user growth value, vi
of users to be predicted.

* is the predicted user growth value by models and N is the number

Primary Model

Sampling k features for K times

Sample 1

Feature 1 Feature 2

Feature 3

Sampling n
samples for
N times

Sample 2

Feature 1 Feature 2

Feature 3

…

Feature k

Sample n

Feature 1 Feature 2

Feature 3

…

Feature k

Figur 5. Bagging of primary model.

4.3.1 Multi-layer Perception (MLP)

We use the framework of tensorflow [31] to perform this model, which can easily modify the optimized
loss to be the same with the evaluation score. Since we only have 1,000 Proben, which can easily cause
overfitting, our MLP model only contains the input layer and output layer. The performance of this primary
model achieves a prediction score of 0.736. To ensure the effectiveness of extracted features, we calculate
the variance of each feature weight among N * K trained models, and filter out the features with higher
variance of model weight (average weight/variance >30%). We assume that the features with higher variance
of model weight are not truly effective features. We finally preserve 41 features in this primary model and
the final prediction score reaches 0.752 (Tisch 6), which indicates a significant improvement compared
with the one without feature filtering.

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Tisch 6. Performance of models.

Modell

Random Forest
XGBoost
MLP
SVR
Semi-supervised regression-MLP
Semi-supervised regression-SVR
Stacking

4.3.2 Support Vector Regression (SVR)

Score

0.737
0.750
0.752
0.754
0.753
0.755
0.764

Since the evaluation score does not have the second order derivative, the next three primary models

cannot optimize toward the evaluation score. We adopt an approximated loss function, d.h.,

loss
ich

(

log v
(

)

ich

−

log v
(

2
)) ,

*
ich

(7)

where vi is the true user growth value, vi
only when

* is the predicted user growth value. SVR [32] calculate the loss

(
v

ich

)

−

log

(
v

ich

> e
,

|log

(8)

where e is a super parameter that needs to be manually adjusted. We use grid search to find the best
parameter for this primary model, resulting in the prediction score = 0.754 (Tisch 6). This primary model
achieves similar prediction performance with the MLP model.

4.3.3 XGBoost

XGBoost [23] is an optimized variant of Gradient Boosting Decision Tree, which is much faster than the

traditional one. We also use grid search to find the best super parameters, which reach a score of 0.750.

4.3.4 Random Forest

Random Forest regression model is composed of many decision trees and uses the average result of each
tree as the final output [33]. Ähnlich, we use grid search to find the best super parameters, and reach a
prediction score of 0.737. This primary model’s performance is not so competitive, which may be due to
the complexity of this model, causing great difficulty when tuning parameters.

4.4 Model Ensemble

Since we only have 1,000 samples with labels, this also poses a great challenge to the learning results
of our model. We propose two ways to overcome this challenge: use semi-supervised regression to extend
the samples with labels, and adopt the stack algorithm to ensure the generality of our model.

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4.4.1 Semi-Supervised Regression

According to the proposed idea of co-training for the regression problem [34,35], we choose the two
best models, d.h., MLP and SVR, to extend the samples with labels. The specific description of the proposed
semi-supervised regression algorithm is shown in Algorithm 1. When running this algorithm, we set the
maximum iterations T equal to 500, and the maximum number of samples K that the cache pool can hold
equals 200. Jedoch, it is quite slow to run the semi-supervised regression, averagely adding 10 Proben
with labels after one day. Because of time constraints, we extend only the data set to 1,066 samples with
labels. After extending the data set, both the performance of MLP and SVR have been slightly improved,
whose evaluation score has been increased by 0.001, jeweils (Tisch 6).

Algorithm 1. Semi-supervised regression algorithm.

Input:
MLP_label: Samples with label for MLP
SVR_label: Samples with label for SVR
T: Maximum iterations
Process:
Repeat for T iterations:

MLP_unlabel: Samples without label for MLP
SVR_unlabel: Samples without label for SVR

K: Maximum number of samples that the cache pool can hold

#train MLP with MLP_label
MLP = Train_model (MLP_label)
#predict the label of SVR_unlabel with MLP
SVR_unlabel_pre = Predict (MLP, SVR_unlabel)
#select K samples from SVR_unlabel_pre as the cache pool for SVR
SVR_unlabel_cache = Sampling (SVR_unlabel_pre, K)
# train SVR with SVR_label
SVR = Train_ model (SVR_label)
#predict the label of MLP_unlabel with SVR
MLP_unlabel_pre = Predict (SVR, MLP_unlabel)
# select K samples from MLP_unlabel_pre as the cache pool for MLP
MLP_unlabel_cache = Sampling (MLP_unlabel_pre, K)
#select sample from cache pool for MLP to extend the samples with label for MLP
for sample_i in MLP_unlabel_cache:

new_MLP =Train_model (sample_i + MLP_label)
Loss_i = model_loss (sample_i + MLP_label)

selected_MLP_i = arg min (Loss_i)
#select sample from cache pool for SVR to extend the samples with label for SVR
for sample_i in SVR _unlabel_cache:

new_SVR =Train_model (sample_i + SVR_label)
Loss_i = model_loss (sample_i + SVR_label)

selected_SVR_i = arg min (Loss_i)

4.4.2 Stacking

Stacking [36] is a type of ensemble learning, which can combine primary models to generate more robust
results. Here we adopt the above-mentioned four single models, d.h., MLP, SVR, Random Forest, XGBoost,
and Semi-Supervised Regression-MLP and Semi-Supervised Regression-SVR which have been extended to

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the training set, as our primary models. We train another SVR based on these models’ output as features. Der
final model after stacking achieves the best performance, reaching a prediction score which equals 0.764.

5. CONCLUSIONS AND FUTURE WORK

This paper describes our work for User Profi ling Technology Evaluation Campaign in SMP Cup 2017. In
the keyphrase extraction task, we treat keyphrase extraction as a classification problem and use the XGBoost
model to predict the top three keyphrases. By this method, we finally obtain the best score among all the
Teilnehmer. In the task of user tagging, two challenges are tackled in our two-phase model. In the first
Phase, which is independent binary classifications, we encode the high dimensional features into the low
dimensional representations in a supervised way. Due to insufficient labelled data, our model has fewer
parameters than traditional softmax-classifer model and avoids overfitting to some extent. In the second
Phase, neural network with softmax as loss function assigns the most likely labels to users. The second
phase models the correlations between labels implicitly, and makes the probabilities predicted by the
independent binary classifiers comparable. In the task of user growth value prediction, we overcome the
lack of training data sets via introducing a semi-supervised regression model and the stacking technique.
Experiments demonstrate the effectiveness of our framework for user growth value prediction with an
accuracy of 0.764.

In the future, we will further improve the performance on user profiling. For keyphrase extraction, mehr
state-of-the-art models modeling the representations of documents can be considered to extract more
Merkmale, z.B., LSTM, hierarchical attention models and so on. For user tagging, we can further explicitly
exploit the structures between each class of users, z.B., the taxonomy of labels, to improve the performance
of multi-label classification. For user growth value prediction, methods based on point process can be
employed to capture the characteristics of user growth value with time going by.

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BEITRÄGE DES AUTORS

All of the authors contributed equally to the work. H. Shen (shenhuawei@ict.ac.cn, Korrespondierender Autor)
is the coordinator of the research group, and designs the whole framework for user profiling. G. Xing
(xingguoliang@ict.ac.cn) and X. Yue (yuexinyu@ict.ac.cn) are the co-contributors for the task of keyphrase
extraction. H. Gao (gaohao@ict.ac.cn) and B. Xu (xubingbing@ict.ac.cn) are the co-contributors for the task
of user tagging. Q. Cao (caoqi@ict.ac.cn) and K. Cen (cenketing@ict.ac.cn) are the co-contributors for the
task of user growth value prediction. All authors contributed to writing and revising this manuscript.

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DANKSAGUNGEN

The work is supported by the National Natural Science Foundation of China (NSFC) under grant numbers
61472400, 91746301 Und 61802371. H. Shen is also funded by K.C. Wong Education Foundation and the
Youth Innovation Promotion Association of the Chinese Academy of Sciences.

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User Proﬁ ling for CSDN: Keyphrase Extraction, User Tagging and User Growth Value Prediction

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User Proﬁ ling for CSDN: Keyphrase Extraction, User Tagging and User Growth Value Prediction

BIOGRAPHIE DES AUTORS

Guoliang Xing received his Master’s Degree from Institute of Computing
Technologie, Chinese Academy of Sciences (CAS) In 2013. He is currently
working at Baidu Online Network Technology (Peking) Co., Ltd. His research
interests include data mining and topic detection.

Hao Gao is currently a PhD student in the Key Laboratory of Network Data
Science and Technology, Institute of Computing Technology, Chinese Academy
of Sciences (CAS). He received his Bachelor Degree from Northwestern
Polytechnical University in 2015. His main research interests include social
computing and Web data mining.

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Qi Cao is currently a PhD student in the Key Laboratory of Network Data
Science and Technology, Institute of Computing Technology, Chinese Academy
of Sciences (CAS). She received her Bachelor Degree from Renmin University
of China in 2015. Her research interests include social media computing,
influence modeling and information diffusion prediction.

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157

User Proﬁ ling for CSDN: Keyphrase Extraction, User Tagging and User Growth Value Prediction

Xinyu Yue received his Master’s Degree from Institute of Computing
Technologie, Chinese Academy of Sciences (CAS) In 2018. Xinyu Yue is
currently working at Bytedance Technology Co. Ltd. His research interests
include recommendation system and data mining.

Bingbing Xu is currently a PhD student in the Key Laboratory of Network
Data Science and Technology, Institute of Computing Technology, Chinese
Akademie der Wissenschaften (CAS). She received her Bachelor Degree from Harbin
Institute of Technology in 2016. Her research interests include geometric
deep learning, graph-based data mining and graph convolution.

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Keting Cen is currently a PhD student in the Key Laboratory of Network Data
Science and Technology, Institute of Computing Technology, Chinese Academy
of Sciences (CAS). He received his Bachelor Degree from Harbin Institute of
Technology in 2016. His research interests include network embedding,
semi-supervised learning on graph and graph convolutional networks.

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User Proﬁ ling for CSDN: Keyphrase Extraction, User Tagging and User Growth Value Prediction

Huawei Shen received his PhD Degree in Computer Science from Institute
of Computing Technology, Chinese Academy of Sciences (CAS). He is now
a professor in the Key Laboratory of Network Data Science and Technology,
Institute of Computing Technology, CAS. His research interests include
social network analysis, social media analytics, network data mining and
scientometrics. He has published over 100 research papers in prestigious
international journals and conferences.