Directed Graph-based Researcher Recommendation by Random Walk with Restart and Cosine Similarity

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SCIS 2020
2020.12.05
Directed Graph-based Researcher Recommendation
by Random Walk with Restart and Cosine Similarity
Kanta Nakamura and Kazushi Okamoto

Department of Informatics, Graduate School of Informatics and Engineering,

The University of Electro-Communications

1

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2020.12.05 SCIS 2020
Introduction
Finding appropriate research collaborators

• to success a research project

• to obtain new knowledge

• to support corporate activities and
 
University Research Administrators (URA)

Approaches of researcher recommendations
2
？？
network-based recommendation content-based recommendation

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2020.12.05 SCIS 2020 3
Usage Situations of a Recommender System for
Research Collaborators
• query: past collaborations, research keywords, the user pro
fi
le

• recommendation result: recommended researcher list
user

(company)
URA
recommender

system
research plan query
recommendation

result
query
recommendation

result
recommendation

result by URA

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2020.12.05 SCIS 2020
Approaches of Researcher Recommendations
Network-based recommendation

• for each researcher, scores among the other researchers are
calculated with observed links (which connect to the researcher)

• number of researchers in common

• Jaccard coef
fi
cient

• effective for a researcher recommendation in the same
fi
eld

Content-based recommendation

• recommendations based on similarity of researcher pro
fi
les

• effective for a researcher recommendation across the 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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2020.12.05 SCIS 2020
• fewer edges between different
fi
elds than that in the same
fi
eld

• lower accuracy for interdisciplinary researcher recommendations

• directed researcher network to consider research roles

• new edges based on researcher similarity
Problem of Network-based Recommendation
and Purpose
5
・proposing models with added edges

・veri
fi
cation the improvement of recommendation accuracy

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2020.12.05 SCIS 2020
Proposed Network Model
6
2
a
b
c
d
e
1
2
1
3
1
2
a b c d e
a 0 0 0 0 0
b 0.25 0 0 0.25 0.5
c 0 0 0 0 0
d 0 1 0 0 0
e 0 0 0.33 0.67 0
a b c d e
a 0 0 0 0 0
b 1 0 0 1 2
c 0 0 0 0 0
d 0 1 0 0 0
e 0 0 1 2 0
normalize
co-researcher principal researcher
weight (# of collaborations)

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2020.12.05 SCIS 2020
Random Walk with Restart (RWR)
• applying RWR to weighted adjacency matrix to calculate
recommendation scores

• recommendation scores of other researchers for researcher
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u
7
<latexit sha1_base64="cb2n6KWT9POypB8Bbuueqq/HAkg=">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</latexit>
rn = cAT
rn 1 + (1 c)qu
: recommendation scores

: iteration

: transition probability

: weighted adjacency matrix

: one-hot vector ( th element is 1)
<latexit sha1_base64="Ev6gy5ad956SnT6iaup1Ev4sIDY=">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</latexit>
r
<latexit sha1_base64="Phrx83swOcfiO1/bdCk4Hdx4CF0=">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</latexit>
n
<latexit sha1_base64="qRtlxaHPUxbfMISOYqH4gdCQ7GI=">AAACZHichVFNLwNBGH66vqqKIhKJRERDnJq3QoiTcHFUVSQ0ze4YbOxXdrdNqvEHuBIHJxIR8TNc/AEHf0AijpW4OHh3u4nQ4J3MzDPPvM87z8xojqF7PtFTTGlpbWvviHcmupLdPb2pvv51zy67QhaEbdjupqZ60tAtWfB135CbjitVUzPkhnawFOxvVKTr6ba15lcdWTTVPUvf1YXqM5UTpVSaMhTGaDPIRiCNKFbs1A22sQMbAmWYkLDgMzagwuO2hSwIDnNF1JhzGenhvsQREqwtc5bkDJXZAx73eLUVsRavg5peqBZ8isHdZeUoxumRbqlOD3RHL/Txa61aWCPwUuVZa2ilU+o9Hsq//6syefax/6X607OPXcyFXnX27oRMcAvR0FcOz+v5+dXx2gRd0Sv7v6QnuucbWJU3cZ2TqxdI8Adkfz53M1ifymRnMpSbTi8sRl8RxzDGMMnvPYsFLGMFBT5X4gSnOIs9K0llQBlspCqxSDOAb6GMfALNnonn</latexit>
c
<latexit sha1_base64="NwCfYxxbeQ4S0+g6oEJRFJtPNsk=">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</latexit>
A
<latexit sha1_base64="DGImgOoEU8vjnyQI1nZzVkdju4A=">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</latexit>
qu
u

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2020.12.05 SCIS 2020
Edges based on the number of collaborations

Edge

Weighted adjacency matrix
Baseline: History Model
8
<latexit sha1_base64="oed20XZ1iHnYwgsP0nF8mCfB8uI=">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</latexit>
eh(u, v) = |Wu,v|
<latexit sha1_base64="jPRPDi0lA7BvGdmwRQKX3V+JSgw=">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</latexit>
eh(u, v) 6= eh(v, u)
<latexit sha1_base64="DfKKhfrY0NSQqmZFo/CdA2hdiK0=">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</latexit>
Ah =
0
B
B
B
@
eh(1,1)
P|V |
i=1 eh(1,i)
· · · eh(1,|V |)
P|V |
i=1 eh(1,i)
.
.
.
...
.
.
.
eh(|V |,1)
P|V |
i=1 eh(|V |,i)
· · · eh(|V |,|V |)
P|V |
i=1 eh(|V |,i)
1
C
C
C
A
: collaborative research set of researchers and
u <latexit sha1_base64="ln+Ew/TgY2XHm2Bd2654z6g/qmQ=">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</latexit>
v
<latexit sha1_base64="TbcvsV2w0HPdXJ0wCDIH54CMYxE=">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</latexit>
Wu,v

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2020.12.05 SCIS 2020
Edges based on research similarities

Edge

Proposal: Similarity Model (1/2)
9
<latexit sha1_base64="DZXNDxmuwRq4zrS6Gk8muaK67jo=">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</latexit>
es(u, v) =
(
xT
u ·xv
||xu||·||xv|| u 6= v
0 u = v
<latexit sha1_base64="MKZHtFNB3rEwS1c8EjWSISOyK0M=">AAACdnichVHLSsNAFD2Nr1pfrW4EQcRSHyDlVhRFEEQ3LuujKqiUJI4amiYhL6nFH/AHXIgLBS3iZ7jxB1z4CeJSQRcuvEkDoqLeYWbOnLnnzpkZxdI1xyV6iEkNjU3NLfHWRFt7R2dXMtW95pierYqCauqmvaHIjtA1QxRczdXFhmULuazoYl0pLQT7676wHc00Vt2KJbbL8p6h7Wqq7DJVTKZE0RnxxvzR2QD4Y95oMZmmLIUx8BPkIpBGFHkzWcMWdmBChYcyBAy4jHXIcLhtIgeCxdw2qszZjLRwX+AICdZ6nCU4Q2a2xOMerzYj1uB1UNMJ1SqfonO3WTmADN3TNT3THd3QI73/Wqsa1gi8VHhW6lphFbuOe1de/1WVeXax/6n607OLXUyHXjX2boVMcAu1rvcPT55XZpYz1SG6oCf2f04PdMs3MPwX9XJJLJ8iwR+Q+/7cP8HaeDY3maWlifTcfPQVcfRhECP83lOYwyLyKPC5BzjDFWqxN6lfykjD9VQpFml68CUk+gAJ6o/Q</latexit>
es(u, v) = es(v, u)
: TF-IDF vector for researcher ’s titles,

keywords and abstracts of projects
<latexit sha1_base64="SNpGGcGNuezorlSZdAbMHuH8A2k=">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</latexit>
xu
<latexit sha1_base64="iwqBmWimQdIwp4BYpQUaNRssknE=">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</latexit>
As =
0
B
B
B
@
es(1,1)
P|V |
i=1 es(1,i)
· · · es(1,|V |)
P|V |
i=1 es(1,i)
.
.
.
...
.
.
.
es(|V |,1)
P|V |
i=1 es(|V |,i)
· · · es(|V |,|V |)
P|V |
i=1 es(|V |,i)
1
C
C
C
A

/ 21
2020.12.05 SCIS 2020
Proposal: Similarity Model (2/2)
Two types of thresholds for calculation cost

• : remove edges when weight is lower than or equal

• : remove edges other than the top weights of each row
<latexit sha1_base64="KgrVebXGxa44Eu0QvBWRKqP7g08=">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</latexit>
t
t
<latexit sha1_base64="Phrx83swOcfiO1/bdCk4Hdx4CF0=">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</latexit>
n <latexit sha1_base64="Phrx83swOcfiO1/bdCk4Hdx4CF0=">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</latexit>
n
10
a b c d e
a 0 0.7 0.2 0.1 0.3
b 0.7 0 0.1 0.2 0.5
c 0.2 0.1 0 0.1 0.4
d 0.1 0.2 0.1 0 0.9
e 0.3 0.5 0.4 0.9 0
input
<latexit sha1_base64="d0C8jR2okdVOMgD/0QEpHA3rxSc=">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</latexit>
t = 0.3
a b c d e
a 0 0.7 0 0 0
b 0.7 0 0 0 0.5
c 0 0 0 0 0.4
d 0 0 0 0 0.9
e 0 0.5 0.4 0.9 0
a b c d e
a 0 0.7 0 0 0.3
b 0.7 0 0 0 0.5
c 0.2 0 0 0 0.4
d 0 0.2 0 0 0.9
e 0 0.5 0 0.9 0
<latexit sha1_base64="BXPiITuRPIfn4vugkd7mFkCJq58=">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</latexit>
n = 2
normalized normalized

/ 21
2020.12.05 SCIS 2020
A model that combines the history model and the similarity model

Edges

• ,


• edges with mean weight of history and similarity models

• setting thresholds and as in the similarity model
<latexit sha1_base64="Cotgi/USEDu+WY0eUwdhlbwyfTA=">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</latexit>
eh(u, v)
<latexit sha1_base64="11Kbnh1rt0jVG0GKR9PDZJxKWqg=">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</latexit>
es(u, v)
t <latexit sha1_base64="Phrx83swOcfiO1/bdCk4Hdx4CF0=">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</latexit>
n
Proposal: Combination Model
11
<latexit sha1_base64="B7jWssnyuJnhyTgsiTOUQw2aEes=">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</latexit>
Ac =
1
2
(Ah + As)

/ 21
2020.12.05 SCIS 2020
Grants-in-Aid for Scienti
fi
c Research (KAKEN) DB
12
details of database
publication year FY1964 - FY2018
number of grants 869,081
number 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

/ 21
2020.12.05 SCIS 2020
Data Collection & Researcher Pro
fi
les Construction
13
crawling

(KAKEN API)
morphological
analysis

(MeCab)
researcher
pro
fi
les
grant awards
grant award data

・started in FY2001-FY2017

・title, keywords, abstract

・principal, co-researcher

・researcher number
researcher pro
fi
les

・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

/ 21
2020.12.05 SCIS 2020
Hyper-parameter Tuning and Test (1/2)
• hyper-parameter tuning:

• researcher network from 2001-2014 projects

• recommendation for researchers in 2015 projects

• test:

• researcher network from 2001-2015 projects

• recommendation for researchers in 2016, 2017 projects
14
year (FY) 2001 2014 2015 2016 2017
hyper-parameter tuning
(number of researchers)
145,045 500
test

(number of researchers)
150,534 809

/ 21
2020.12.05 SCIS 2020
Hyper-parameter Tuning and Test (2/2)
Set of true recommended researchers for :

• construction from 2015 or 2016-2017 projects

Recommendation accuracy metric: nDCG@k

• nDCG@k:

Hyper-parameter tuning

• threshold

• threshold
u
15
<latexit sha1_base64="xRl6q42L7Owsa9cSkbJEKmonwto=">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</latexit>
d = I(v1 2 V T
u ) +
k
X
j=2
I(vj 2 V T
u )
log2 j
<latexit sha1_base64="vc82K6U/s044Kut5//yMEd+hDpM=">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</latexit>
d/dt : maximum value of
<latexit sha1_base64="iEqKpg2yv8Ae2UBfMZlhG5yl/C0=">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</latexit>
d
<latexit sha1_base64="U3vvwUeRqTh+jjaU4FZ0NknJcwQ=">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</latexit>
dt
<latexit sha1_base64="PvLQEcU5Y2Huc3S8T2PENJK1u9E=">AAACfHichVHLLgRBFD3T3uM1WJDYiEEk6NwRr1gJG0uvQWJk0t1qqOjp7nTXTMLED/gBCytERPAVNn7AwieIJYmNiDs9nQiC27ldp07dc+tUlenZMlBEDzGtqrqmtq6+Id7Y1NzSmmhrXw3cgm+JtOXarr9uGoGwpSPSSipbrHu+MPKmLdbM3bny+lpR+IF0nRW154nNvLHtyJy0DMVUNtGpMtLJlEgfHSZ9jHOCcypzkE0kSacwen6CVASSiGLBTVwggy24sFBAHgIOFGMbBgL+NpACwWNuEyXmfEYyXBc4QJy1Ba4SXGEwu8v/bZ5tRKzD83LPIFRbvIvN6bOyB/10T5f0THd0RY/09muvUtij7GWPR7OiFV629bBr+fVfVZ5HhZ1P1Z+eFXKYCr1K9u6FTPkUVkVf3D96Xp5e6i8N0Ck9sf8TeqBbPoFTfLHOF8XSMeL8AKnv1/0TrI7qqXGdFseSM7PRU9SjG70Y5PuexAzmsYB0uO8ZrnETe9f6tCFtpFKqxSJNB76ENvEBYEKQuA==</latexit>
t 2 {0.2, 0.4, 0.6, 0.8}
<latexit sha1_base64="Q6AKLPsScz8CEdOQPGEzR8BGBow=">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</latexit>
n 2 {10, 20, 30, 40, 50}
<latexit sha1_base64="PMlbppsOFRBQ1sNlBLCVAPHCouE=">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</latexit>
V T
u

Hyper-parameter Tuning Results
similarity model (t) similarity model (n)
combination model (t) combination model (n)

/ 21
2020.12.05 SCIS 2020
Test: Comparison of nDCG@k
17

/ 21
2020.12.05 SCIS 2020
Network of History and Similarity Model
• threshold n has fewer edges than threshold t

• edge distribution may be important
18
model # of nodes # of edges mean degree
degree
variance
history 1.51×105 2.90×105 1.93 3.83
similarity
t=0.4
1.51×105 3.68×106 24.4 4.59×103
similarity
n=10
1.51×105 1.51×106 10 0

/ 21
2020.12.05 SCIS 2020
Degree Distribution of History and Similarity Model
19

/ 21
2020.12.05 SCIS 2020
Future Work
Inverse adjacency matrix calculation

• RWR can be calculated simply by using the inverse matrix of the
adjacency matrix

• since the inverse matrix is dense, it is necessary to reduce the
memory size

Validation of combination weights

• adding hyper-parameter
20
<latexit sha1_base64="0Bd1nxnJZC2JCiku0ifA6JFUdGE=">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</latexit>
lim
n!1
rn = (1 c)(I cAT
) 1
qu
<latexit sha1_base64="kKkF+KjMx7PutR/T4J3Ba8Iq8KE=">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</latexit>
Ac = ↵Ah + (1 ↵)As
<latexit sha1_base64="LhDn+NUhHlu1JYi4vmi3/sCRTU0=">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</latexit>
↵ = (0, 1)

/ 21
2020.12.05 SCIS 2020
Conclusion
Purpose

• proposing models with added edges

• veri
fi
cation the improvement of recommendation accuracy

Results

• highest recommendation accuracy
 
by combination model of history edge and similarity edge

• nDCG@k may decrease with increasing the number of edges

• the distribution of degrees is important

Future work

• inverse adjacency matrix calculation

• validation of combination weights
21

Directed Graph-based Researcher Recommendation by Random Walk with Restart and Cosine Similarity

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