Kanta Nakamura, Kazushi Okamoto: Development of a Collaborator Recommender System Based on Directed Graph Model, 20th International Symposium on Advanced Intelligent Systems and International Conference on Biometrics and Kansei Engineering (ISIS2019&ICBAKE2019), 2019.12

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ISIS 2019
2019.12.06
Development of a Collaborator Recommender System
Based on Directed Graph Model
Kanta Nakamura and Kazushi Okamoto
Department of Informatics, Graduate School of Informatics and Engineering,
The University of Electro-Communications
1

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2019.12.06 ISIS 2019
Introduction
Importance of utilizing new technology in companies:
• New entry and intensifying international competition
• Shortening life cycle of products and services
• Insuf
fi
cient in-house technology and management resources
Effective use of collaborative research with external organizations
• Selection of researchers who
fi
t purposes
• Researcher recommendation methods
• Collaborator recommender systems
2

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2019.12.06 ISIS 2019 3
Use of Recommender Systems
When companies use a system directly or
as a University Research Administrators (URA) support
Presented by ranking based on recommendation score
user
(company)
URA
recommender
system
research plan query
recommendation
result
query
recommendation
result
recommendation
result

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2019.12.06 ISIS 2019
Researcher Recommendation Methods
Network-based recommendation
• Recommendations based on indicators calculated from a link
structure around a researcher pair
• Number and ratio of researchers in common
• Effective for recommendations in the same
fi
eld
Content-based recommendation
• Recommendations based on similarity
of researcher pro
fi
les
• Effective to recommend researchers
across different
fi
elds
4
？
？
Masataka Araki, Marie Katsurai, Ikki Ohmukai, and Hideki Takeda: Interdisciplinary collaborator
recommendation based on research content similarity, IEICE Transactions on Information and
Systems, vol.E100.D, no.4, pp.785-792, 2017.
[araki+, 2017]

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2019.12.06 ISIS 2019
Researcher Network and Link Prediction
Undirected graph model: researcher contributions are equal
Directed graph model: researcher contributions are not equal
• Ex)
fi
rst author → Nth author, principal researcher → co-researchers
5
: researcher (node)
: known edge
: unknown edge
undirected graph model directed graph model

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2019.12.06 ISIS 2019
Purpose and Proposed Method
Veri
fi
cation of three directed graph models in researcher recommendation
6
i
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j
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principal researcher co-researcher
y 2 {0, 1}
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<latexit sha1_base64="DfOOSE2AzLGcz9VuxPXiSKGFPuM=">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</latexit>
node vector
edge vector
Outer product model:
Joint model:
Weighted sum model:
xO = xi ⌦ xj
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xi, xj
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xW = ↵xi + (1 ↵)xj, ↵ 2 (0, 1)
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xJ = [xi, xj]
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2019.12.06 ISIS 2019
Classi
fi
er and Optimizer
• Output probability
• Interpretation using partial regression coef
fi
cients
• Batch processing can handle large amount of data
• Minimize a log loss function
7
p✓(y = 1|x) = 1
1+exp(✓T ·x)
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Logistic Regression (LR)
Stochastic Gradient Descent (SGD)
LogLoss = {y log p + (1 y) log(1 p)}
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2019.12.06 ISIS 2019
Overview of the Proposed System
8
outer product
joint
weighted sum
researcher
pro
fi
le set LR
BoW & LDA
node vector edge vector ✓
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W
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outer product
joint
weighted sum
researcher
pro
fi
le set
LR
BoW & LDA
query
score
training
prediction

9. / 16
2019.12.06 ISIS 2019
Grants-in-Aid for Scienti
fi
c Research (KAKEN) DB
9
details of database
publication year FY1964 - FY2018
# of grants 869,081
# of researchers 251,333
characteristics
・Japanese DB
・possible to collect grant awards
・uniquely identi
fi
es researchers
・researcher roles (principal, co-researcher)
・KAKEN API for data collection
As of March 2019

10. / 16
2019.12.06 ISIS 2019
Collect Data and Create Researcher Pro
fi
les
10
crawling
(KAKEN API)
morphological
analysis
(MeCab)
researcher
pro
fi
les
grant awards
grant award data
・started in FY1964-FY2017
・title, keyword, abstract
・principal, co-researcher
・researcher number
researcher pro
fi
les
・[researcher number].txt
・nouns only
・space separator
地形 データ 沖 地震
北部 震源 対象 地磁気 地下
水中 震源 以深 ヘリウム
地殻 流体 マントル 起源
流体 震源 筆者 地殻 均質
構造 概念 モデル 構造 均質
弾性 モデル 数値 地域 …
EX) 80421684.txt

11. / 16
2019.12.06 ISIS 2019
Generation Samples and Hyper-parameter Tuning
Hyper-parameter tuning
• The number of LDA topics : {50, 100, 150, 200}
• Outer product model : 150
• Joint & weighted sum model : 200
Weighted sum model: α
• α = 0.7 (
fi
xed)
11
starting year (FY) 1964 2012 2013 2014 2015 2017
hyper-parameter
tuning
training edges
4,890,952
test edges
46,046
method evaluation
training edges
5,373,665
test edges
50,512

12. / 16
2019.12.06 ISIS 2019
Prediction Accuracy for Each Model
Predict a link based on a probability calculated by logistic regression:
12
model
outer product joint weighted sum
precision 0.553 0.093 0.094
recall 0.693 0.393 0.377
f1 score 0.615 0.150 0.150
yij =
(
1 p(yij = 1|xij) 0.5
0 otherwise.
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2019.12.06 ISIS 2019
Computation Time Evaluation for Each Model
• Server performance used in the experiment
• OS: Ubuntu 18.04.1
• CPU model: dual Intel Xeon CPU E5-2697 v3 @ 2.60GHz
• # of cores: 14
• RAM size: 128 GB
13
model [# of edge vector dimensions]
outer product
[22,500]
joint
[400]
weighted sum
[200]
edge vector construction
per edge [s]
1.87×10−5 1.45×10−6 1.61×10−6
learning [s] 5140 94 49
predicting per edge [s] 1.44×10−6 2.36×10−8 1.14×10−8

14. / 16
2019.12.06 ISIS 2019
Analysis of Partial Regression Coef
fi
cient (OP)
• A partial regression coef
fi
cient is high between same topics
• Some pair of different topics have high partial regression coef
fi
cients
14

15. / 16
2019.12.06 ISIS 2019
Analysis of Partial Regression Coef
fi
cient (JN & WS)
• Unlike the outer product model, it is dif
fi
cult to
fi
nd characteristics
• Topic co-occurrence between researchers is important for link
prediction
15
joint model weighted sum model

16. / 16
2019.12.06 ISIS 2019
Conclusion
Purpose
• Veri
fi
cation of the three directed graph models in researcher
recommendation
Results
• The outer product model achieve highest prediction accuracy
• Topic co-occurrence is important for link prediction
Future work
• Model construction by unsupervised learning
• Veri
fi
cation of data sampling method
16