Author’s Paper Similarity Prediction based on the Similarity of Textual
References to Visual Features
Mostafa Alli
Dept. of Computer Science and Technology, Tsinghua University, Beijing 100084, China
Keywords:
Paper Prediction, Common Coauthors, Textual Reference, Visual Feature, Table, Figure, Boolean Function,
Sensitivity, Scientific Stop Word.
Abstract:
In this paper we introduce a mechanism to find similar papers of an author, based on the author’s previous
1
publications. In other words, since the author(s) of a paper are more likely to publish similar work(s) to their
paper, we use this intuition to seek related papers based on the visual similarity of those papers. The visuality
here is the figures and or tables that are commonly used by authors to describe their method structure and/or
the result of their experiments. Since similar works of authors are focused on solving similar problems as well
as developing and improving similar techniques, we noticed that comparing these visual features among their
publications would help to spot most similar papers of those authors. We call our method, Similarity of Textual
References to Visual Features which means, we compare parts of content of any two arbitrary papers that have
references to any figures and/or tables. In our experiment we show that how we can use this similarity together
with other factors of a paper to form a Boolean function which helps to build an indexation for papers based
on the number of their authors. In this way, we omit time consuming process of papers’ content determined
analysis, such as, textual content analysis, building coauthor network, citation network etc. In addition, our
Boolean function has the ability of adjusting level of Sensitivity. If we want to achieve higher accuracy of
similar papers, the Boolean function needs to be enabled for more
2
conditions.
1 INTRODUCTION
Ultimate recommending similar papers will be a lot
of help and will reduce the effort, time and the chance
of missing related publications for researchers. As a
solution, there are numerous application to suggest
related papers based on a user profile (Hong et al.,
2013a)(Hong et al., 2013b), citation data (Bogers and
van den Bosch, 2008)(Ma et al., 2008), a combination
of page rank algorithm (Brin and Page, 1998) and
citation network (Nykl et al., 2014) etc. Nonetheless,
these techniques may not work well since this is
shown (Vellino, 2009)(Vellino, 2010) that using page
rank algorithm values will not improve the similarity
measurement as well as a user profile technique needs
a huge set of data and documents to work with, and
a content-based filtering for paper suggestion is time
consuming since it goes through a whole text (He
et al., 2010). In addition, this can be argued that
1
Or inversely, future published work, depending to the
paper selection.
2
Or even all
a citation oriented system deliberately ignores two
facts ,i.e, a recent and similar paper has few-or even
no- citation score, while, a paper with broad focus
but less similarity, such as survey papers, has gained
much more citations. Study (Pohl et al., 2007) shows
that a paper has very low citation within its first 2
years of its publication. That means, a paper, at
least, needs 2 years time to be started to be seen by
researchers. However, this is not the only drawback
of citation-based scheme. Such systems suffer from
Matthew Effects (Stanovich, 1986) which is, rich
gets richer and poor gets poorer. In other words,
the paper with more citation gets more attention and
then gets more cites and one with less cites, will
be ignored for some more time. There is another
shortcoming of a citation-based and similar systems
which is the coverage. According to (Good et al.,
1999), the coverage of a recommender system is
a critical factor for its accuracy. Since a citation
based system will ignore a significant number of
items(papers) (He et al., 2010), the accuracy would
be decreased significantly too.
Although in a recent study (Sayyadi and Getoor,
Alli, M..
Author’s Paper Similarity Prediction based on the Similarity of Textual References to Visual Features.
In Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2015) - Volume 1: KDIR, pages 637-643
ISBN: 978-989-758-158-8
Copyright
c
 2015 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
637

2009) authors tried to adjust some of the short-
comings of citation-based systems such as Matthew
Effects which is also called Slow start for citation
count, however, their assumptions for doing such
are still representing the Matthew effect issue, e.g,
Good research papers are written by researchers
with high-reputations, Important articles are cited by
many important articles. etc.
In our previous work (Alli, 2015) we introduced
a paper similarity technique which takes into account
the summarization version of each paper. The work
was motivated for the fact that a full-text comparison
is aggressive and slow (He et al., 2010). Moreover
a citation-based system requires complex NLP tech-
niques and needs a long time to obtain enough citation
scores for meaningful similarity analysis (Pohl et al.,
2007).
However, we still think that using either of
existing techniques
3
will not guarantee to take into
account the previous publications of the author(s) of
the input paper. Despite the fact that there are efforts
made to consider this issue, but we believe they are
not so accurate.
A context-aware recommender system (He et al.,
2010) is introduced which considers a set of factors
to build up a group of paper candidates for future
analysis. One of the factors that they consider is
previous publications of an author. However, there
is no selection policy and all of an author’s papers
will be added to the candidate set. Consequently, the
previous publications of other authors of candidate set
will be added and so on. This can be clearly seen that
this Blind technique will add loads of unnecessary
papers and even can lead to infinite loop of paper
addition.
Similarly, a key-phrased based system (Sugiyama
and Kan, 2010) is proposed to recommend papers
based on the users’ interests by considering his or
her previous publication records. To do so, the
system builds up a user profile of researchers based
on their publication record as either senior or junior
researchers. However, this needs to build a profile
for each user which takes extra time and needs to
gather additional data. In addition, such system has
even a much bigger problem. There is no necessity
for someone to narrow down his/her researching
field to one or few topics. It will be so common
that someone changes his/her working direction after
an accomplishment or achievement. Authors has
mentioned that they would put more weight(near to
1) for recent papers and less weight(near to 0) for
older papers to fix this problem. Nonetheless, this is
still possible that an author is already finished his/her
3
including our previous work
work with some recent publication and has started a
new topic which includes no publications record yet
or a work can be followed after a period of time.
As a compromise, we are motivated to specifically
build a system that intellectually finds similar papers
of an author regarded to his/her previous/fututre
4
We
believe that the writer of a research paper is more
likely to publish one or more related or similar paper
based on his/her own paper’s topic. To find out
that which publications must be regarded as similar
ones, we evaluated our method in section 4 where
in first experiment we interrogated the feasibility of
the textual references to visual features and then,
using this methodology, in second experiment, we
investigated 3 different factors in an Author Previ-
ous/Future Publication Similarity Prediction System,
i.e, The placement of author in his/her paper, the year
of publication and the number of common coauthors.
Accordingly, in third experiment, we developed a
Boolean function which will be used for indexing
papers of an author based on the significant value of
those factors, if there is any.
The rest of the paper is organized as follow. In
section 2, we give a review of related work, in section
3, we briefly explain our proposed approach, and
finally in section 5, we gave a conclusion to this work.
2 RELATED WORK
A coauthor similarity system (Han et al., 2013)(Sun
et al., 2011) is where the system tries to find the
similarity between a user’s interests and an author
based on their research concerns. Such system may
or may not try to recommend papers based on this
similarity, however, in case of recommendation, the
system will retrieve many irrelevant papers especially
when predicted authors are working on a wide range
of topics.
J.Tong et al (Tang et al., 2007)(Tang et al., 2008)
proposed to mine social network of researchers. The
aim of this work is to collect researchers information
from web, based on the researcher name and then,
mining the semantic part of the retrieved information
in order to extract useful information. This leads
to build a profile for each researcher which contains
their information, such as publications, affiliations,
emails etc. In addition, this system provides name
disambiguation when there are several researchers
with same name or same abbreviate name. For
name disambiguation task, this system which is called
ArnetMiner, applies a constraint policy which maps 6
4
Depending on the selected paper’s date of publication.
DART 2015 - Special Session on Information Filtering and Retrieval
638

constraints, i.e, CoOrganization, CoAuthor, Citation,
CoEmail,FeedBack from users and τ-CoAuthor, into
one single authors publications to see if the infor-
mation belongs to same person or they are actually
different persons. However, this system lacks the
actual paper recommendation, especially based on the
author’s publications.
Q.He et al (He et al., 2010) proposed a context-
aware system to recommend citation for a citation
placeholder. The method will retrieve a large body
of papers based on some factors such as papers with
most title and abstract similarity, papers that have
similar authors with original paper etc. The time is
one of the most drawbacks of this proposed system
due to the fact that it needs 50-100 seconds time for
every new candidate. Moreover, they do not consider
any constraint for authors’ paper when they add them
to the candidate set. Consequently, there will be a
large number of unnecessary papers that are added to
the candidate set.
H.Chen (Chen et al., 2011) introduced a recom-
mender system which uses coauthoring network to
suggest authors with similar interests. It receives a
query from user and by using CiteSeerX database,
it builds a coauthor network to make collaboration
recommendation. Nevertheless, this system will not
suggest any similar paper to the users.
3 PROPOSED FRAMEWORK
In contrast to the previous researches in paper simi-
larity calculation in order to find similar papers based
on ones publication records, we believe that since the
author(s) of a paper are more likely to publish similar
work to their previous works, we use this intuition
to seek related papers based on the visual similarity.
The concept of visuality here is any visual elements,
e.g, Table and Figures, that are commonly used by
authors to describe their proposed framework and/or
the result of their experiments. Due to the fact that
similar works of coauthors are focused on solving
similar problems as well as developing and improving
similar techniques, we realized that comparing these
visual features among their published researches will
be a great help for finding similar works of those
authors. Nonetheless, the task of image processing
for detecting similar figures and tables is a time
consuming task and requires a high resource capacity.
In addition, some figures, such as flowcharts that con-
sist similar geometrical objects, are visually similar
but are actually different in nature. Consequently,
we decided to use a smarter way to tackle this
obstacle. We called this method, Similarity of Textual
References to Visual Factors, that is, we compare
the part of content of any two arbitrary papers that
has a reference to any figure and/or table. This
similarity can be both visually similar figures/tables
with similar textual references to those figures and
tables(Buyukkokten et al., 2001a; Buyukkokten et al.,
2001b) or just similar textual references to those
visual features (Tang et al., 2009; Wang et al., 2010).
To spot such similar textual references, we use
the String tokenizer to divide the textual content of
a research paper into tokens. Tokens here are a set of
characters that are surrounded between two dots.
As we mentioned previously, we will compare the
similarity of those parts of any two papers that are
referencing to a visual feature. For detecting these
special references, we used 3 Regular expressions
Regex that can demonstrate the common way that
authors refer to any table and figure. This regexes are
illustrated in Listing 1:
Listing 1: Regular expression used for detection of Textual
references to visual features.
St ri ng pa tte rn =" \\ (* [ f F ] i gure \\ s * \\ d *[ :.] *. * " ;
St ri ng pa t te rn 2 = " \\ (* [ tT ] ab le \\ s * \\ d *[: .] *. * " ;
St ri ng pa t te rn 3 = " \\ (* [ fF ] ig \\ s *\\ d * [: . ]* .* " ;
Accordingly, to prevent misinterpreting, we man-
aged to delete English stop words. We introduce a
new set of stop words called, Scientific stop words
which refers to those words that are commonly used
in research papers, e.g, Model, Proposed, Chart, etc..
In evaluation section, in our first experiment, we show
that this similarity comparison results significantly
different values for comparisons between similar and
dissimilar papers of common coauthors. To make
our method even more useful, we investigated the
impact of three factors, i.e, Papers’ Publication
Year, Author’s Position and The Number Of Common
Coauthors, in the result of similarity measurement in
our second experiment. To make the coauthors factor
more general and accurate, we introduce a coauthor
ratio and use it instead of the absolute number of
common coauthor in our third experiment in order to
create a Boolean function which can be used to index
one’s publication. The ratio formula is illustrated in
Equation 1:
CoauthorRatio = Ratio
CandidatePaper
/Ratio
PivotPaper
(1)
Where the Pivot paper indicates the input paper
and the numerator is equal to:
N
CommonCoauthors
− 1/N
TotalAuthorsInCandidatePaper
− 1
(2)
And denominator is equal to:
Author’s Paper Similarity Prediction based on the Similarity of Textual References to Visual Features
639

N
CommonCoauthors
− 1/N
TotalAuthorsInPivotPaper
− 1 (3)
The substraction in both Equation 2 and 3
demonstrates the existence of the first author of the
pivot paper that obviously appears in all of his/her
publications. For example, if the pivot paper has 4
authors in total and an arbitrary paper of first author
of the pivot paper, denotes as P
i
, has total number of
authors of 6 which 3 of them are same with the pivot
paper, the ratio of candidate paper and pivot papers
are equal to Ratio
CandidatePaper
= 3 − 1/6 − 1 = 0.4
and Ratio
PivotPaper
= 3 − 1/4 − 1 = 0.66 and hence
the final coauthor ratio of P
i
will be equal to 0.60.
4 EVALUATION
To evaluate our method, we studied 3 experiments.
In first experiment, we provided a manually selected
dataset of 45 papers to show that how the similarity
of textual references to visual features can help
to indicate one’s similar publications. In second
experiment, we show that how this similarity merit
might be dependent to other factors of a paper, i.e,
Author’s position, Publication year and Coauthor
ratio, and finally, in last experiment, we show that
how we can generalize this method into a Boolean
function which its job is to index one’s publication.
4.1 Experiment 1
As we stated previously, researchers tend to use
similar visual features in their similar works. These
visual features are significantly similarly mentioned
in similar articles of common authors compare to
the rest of their publications. To illustrate this, we
managed to run an experiment over 45 scientific
papers, grouped in 15 categories. Each category
consists of 3 papers from similar coauthors, which 2
of them are similar papers and one of them is not.
We show that the similar papers in each pair, are
significantly using similar references to their visual
features compare to the dissimilar one. To show the
significant effect of our proposed method, we first
extracted the textual references to visual features of
each paper. To do such, we used a String Tokenizer
to divide the whole contextual part of paper into
blocks that are surrounded by two dots. Accordingly,
we remove all the English language stop words and
common words that researchers usually usw in their
work to mention a table or figure, such as Model,
Proposed, Chart etc. We call these type of words,
Scientific stop words. Using the Cosine Similarity,
we obtained the similarity of these references for
each 3 groups for each category, once between first
group and second group, calling it result A
5
and once
between first and third group calling it result B
6
,
and once between second and third group calling it
result C
7
By running ANOVA test once between A
and C and once between B and C, the results are
significant (P-value=0.000 and 0.001 respectively). In
other hands, this can be said that the visual similarity
of authors’ papers based on the textual references are
significantly different between similar and dissimilar
ones.
4.2 Experiment 2
In this experiment, our aim is to use the results
of previous experiment, investigating the relations
between papers of an author based on the 3 factors,
i.e, Author’s position on the paper, Publication’s
year and Number of common coauthor(s). To
do such, we selected an arbitrary author with 41
papers publication as well as 7 journal articles. We
categorized this data set based on the three factors
that we stated previously. By choosing a paper in
the year of 2007, we examined our visual similarity
comparison. Since the data set is highly unbalanced,
we decided to use General Linear Model(GML)
8
to
seek for significance in the observed data. The result
of GLM tests are illustrated for each category at
Table 1, 2 and 3. By looking at Sig column of each
categories’ GLM tests results, we can see that the
author’s position in his/her other publications will not
give any significance impact on similarity based on
the textual references to visual features
9
. To make it
more precise for the other two significant factors that
how much they effect the observed similarities value,
we can obtain the Effect size from the values of Sum
of Squares (SS) from Table 1 and 2 . There are two
ways of measuring the effect size: either of Eta square
or Partial eta square. However, this is suggested
(Levine and Hullett, 2002) to use Eta square
10
since
it will give a more precise value
11
. The formula for
computing the Eta square is illustrated in Equation 4.
5
Similarity value between two similar papers
6
Similarity value between first similar paper and the
dissimilar paper
7
Similarity value between second similar paper and dissim-
ilar paper
8
Not to confuse with Generalized Linear Models
9
Please note that this factor can be significant for another
author or even for a different paper of same author.
10
Or Omega square, Epsilon square
11
Although it will reduce the value of effect size.
DART 2015 - Special Session on Information Filtering and Retrieval
640

η
2
=
SS
treatment
SS
treatment
+ SS
error
(4)
Where SS stands for Sum of Squares. By using
the values of SS reported in Table 1 and 2, we
can calculate the Eta square values for publication
year and common coauthors factors which are 0.444
and 0.392 respectively. Since the eta square is an
analogous of r-square, this means that papers based
on their publication year and number of common
coauthors are similar at the rate of 44.4% and 39.2%
respectively.
Table 1: GML tests’ results for number of common
coauthors category.
Source Sum of F Sig
Squares(III)
Corrected Model 1692.075 11.943 0.000
Number of 1692.075 11.943 0.000
Common Coauthors
Error 2621.097
Total 10139.385
Table 2: GML tests’ results for publication year category.
Source Sum of F Sig
Squares(III)
Corrected Model 1918.761 2.924 0.37
Publication Year 1918.761 2.924 0.37
Error 2994.400
Total 10139.385
Table 3: GML tests’ results for author’s position category.
Source Sum of F Sig
Squares(III)
Corrected Model 334.641 0.736 0.574
Author’s Position 334.641 2.924 0.574
Error 3979.521
Total 10139.385
We can make it more accurate that in which years
are the most similar papers and by how many common
coauthors, we can achieve the most similar papers
regarding to the pivot paper. Consequently, we can
apply two Contrasts that GLM provides, i.e, Helmert
and Deviation Contrast. The former is where the
effect of each category of independent variable is
compared to the mean of the subsequent category
12
while the later is where the effect of each category
13
is compared with the grand mean.
By performing the Helmert contrast upon pub-
lication year category, we see a significant result
only for the group 7 which stands for the year 2009.
That means the year 2009 has a significance effect
12
Except the last one, due to the fact that there will be no
subsequent category to compare with.
13
Except one of them which by default is the last category.
(P-value=0.009) compare to the subsequent category,
which are the years 2005-2008. although there is
no other significant result, we still can not be sure
that only the year 2009 is the year that we should
look for similar papers, however we know that this
year, possibly together with its subsequent categories,
carry a significant effect on the similarity. To make
sure about this, we perform a deviation contrast to
see the effect of each category. The result shows
a significant effect on category 7 (P-value=0.001)
and 9 (P-value=0.40), which are 2009 and 2007
respectively. In other hand, it means that, based on the
paper that we selected from year 2007, we only need
to consider year 2009 and 2007 in order to retrieve
most similar papers.
Since the pivot paper has only 3 coauthors, we
applied a Repeated contrast which compare effect
of each category with its adjacent one. In this way,
we can see that how increase of number of common
coauthor would effect similarity measurement. The
result defines that there is no significant effect by
increasing the number of common coauthor from 0
to 1(P
value
= 0.092) but there is almost significant
effect on similarity when the number of common
coauthors increases from 1 to 2 (P
value
= 0.052). We
can conclude that based on the similarity of textual
references to visual features, in our experimental
settings, the most similar papers of the first author of
pivot paper, published by 2007, are those paper that
are published in year 2009 and 2007 and/or with 2
common coauthors.
4.3 Experiment 3
The aim of this experiment is to use the results of
two previous experiments in order to build a Boolean
function which its task is to index papers of an author.
To do such, we selected 2 arbitrary authors. By
selecting two random papers of each, we used the
stated method to seek for significance results in any
of the 3 factors, author’s position, publication year
and coauthor ratio. Any significant value for each
factor will be added into the Boolean function under
an specific ID. Since showing all of the conditions
of Boolean function would take up a lot of space
and confuse the reader, for being concise we only
demonstrate a part of it as Boolean functions are
illustrated in the Equation 5.
Author’s Paper Similarity Prediction based on the Similarity of Textual References to Visual Features
641
































































A
3
P
4
I
19
= ω(4 −i)[λ(2
ω(|1.2−ratio|)
)∨
λ(2
ω(|1−position|)
∨
2
ω(|3−position|)
∨
2
ω(|4−position|)
)]
A
2
P
5
I
18
= ω(5 − i)[λ(2
ω(|0.28−ratio|)
)]
A
2
P
6
I
27
= ω(6 − i)[λ(2
ω(|0.66−ratio|)
)∨
λ(2
ω(|21−year|)
∨
2
ω(|22−year|)
)]
A
3
P
8
I
30
= ω(8 − i)[λ(2
ω(|2.31−ratio|)
)
∨ λ(2
ω(|19−year|)
∨ 2
ω(|21−year|)
∨ 2
ω(|22−year|)
∨ 2
ω(|23−year|)
)]
(5)
ω(x) =
(
1 x=0
0 else
(6)
λ(x) =
(
1 x = 1
0 else
(7)
Where A
i
P
j
I
k
is a unique ID for paper k of
author i with total number of j authors. The ω(x)
function checks the significant value of each of 3
introduced factors. Note that there might be a case
that a paper had no significant value for any of
factors. That means the paper has no considerable
similarity with other publication of its first author. In
addition, ω(x) takes care of matching correct papers
in terms of number of total authors to correct Boolean
function. Definition of ω(x) is illustrated in Equation
6. ratio is the common coauthor ratio, calculated by
Equation 1 and year stands for the year category of
the selected paper which is 1 for 1988 and 27 for
2015. Each λ(x) returns 1 when x = 1. In other
word, this function’s condition satisfies whenever any
of significant factor of a paper gets enabled. The
Logical or symbol, ∨, indicates that as soon as either
of condition satisfies, the corresponding paper will be
regarded as a significantly similar paper. In order to
reach a better result, we can order each factors based
on its effect size. Steps of calculating effect size is
demonstrated in section 4.2. On the other hand, we
can change the logical or into the Logical and symbol
∧ only between each λ function and not within them.
This means, the Boolean function is true if and only
if all conditions satisfy together.
All Boolean functions of author A
1
can be sum-
marized in Equation:
n
∑
j=3
m
∑
k
A
1
P
j
I
k
(8)
where n indicates the maximum number of au-
thors that in the publication record of author A
1
appears. For any particular P
j
there might be one or
more I
K
that are not necessary valid for all j and k.
The summation starts from 3 since according to the
common coauthor ratio, for papers with 1 authors, the
ratio will be infinite
14
and for papers with 2 authors,
the Equation 3 will be equal to either 1 or 0. When it
is equal to 0, the value for Equation 1 would be equal
to infinite.
5 CONCLUSION AND FUTURE
WORK
In this paper, we first introduced a novel method to
intellectually select one’s similar paper from his/her
other publications. Then we showed the effectiveness
of our method and accordingly, we came into a
Boolean function which is based on the papers’ char-
acteristics, i.e, Author’s position, Publication year
and Coauthor ratio. However for a fully functional
system for paper recommendation that is not only
limited to an authors publication record, we can
combine this system together with our previous work
(Alli, 2015) to build a fast and accurate recommender
system in scientific scholarly field, specialized in
computer science.
REFERENCES
Alli, M. (2015). Papers similarity based on the summa-
rization merits. In 2nd International Conference on
Behavioral, Economic, and Socio-Cultural Comput-
ing (BESC2015)(Accepted).
Bogers, T. and van den Bosch, A. (2008). Recommending
scientific articles using citeulike. In Proceedings of
the 2008 ACM Conference on Recommender Systems,
RecSys ’08, pages 287–290, New York, NY, USA.
ACM.
Brin, S. and Page, L. (1998). The anatomy of a large-scale
hypertextual web search engine. Comput. Netw. ISDN
Syst., 30(1-7):107–117.
Buyukkokten, O., Garcia-Molina, H., and Paepcke, A.
(2001a). Accordion summarization for end-game
browsing on pdas and cellular phones. In Proceedings
of the SIGCHI Conference on Human Factors in
Computing Systems, CHI ’01, pages 213–220, New
York, NY, USA. ACM.
14
The denominator in Equation 3 will be equal to zero
DART 2015 - Special Session on Information Filtering and Retrieval
642

Buyukkokten, O., Garcia-Molina, H., and Paepcke, A.
(2001b). Seeing the whole in parts: Text summa-
rization for web browsing on handheld devices. In
Proceedings of the 10th International Conference on
World Wide Web, WWW ’01, pages 652–662, New
York, NY, USA. ACM.
Chen, H., Gou, L., Zhang, X., and Giles, C. L. (2011).
Collabseer: a search engine for collaboration discov-
ery. In Proceedings of the 2011 Joint International
Conference on Digital Libraries, JCDL 2011, Ottawa,
ON, Canada, June 13-17, 2011, pages 231–240.
Good, N., Schafer, J. B., Konstan, J. A., Borchers, A., Sar-
war, B., Herlocker, J., and Riedl, J. (1999). Combining
collaborative filtering with personal agents for better
recommendations. In Proceedings of the Sixteenth
National Conference on Artificial Intelligence and the
Eleventh Innovative Applications of Artificial Intelli-
gence Conference Innovative Applications of Artificial
Intelligence, AAAI ’99/IAAI ’99, pages 439–446.
Han, S., He, D., Brusilovsky, P., and Yue, Z. (2013). Coau-
thor prediction for junior researchers. In Greenberg,
A., Kennedy, W., and Bos, N., editors, Social Com-
puting, Behavioral-Cultural Modeling and Prediction,
volume 7812 of Lecture Notes in Computer Science,
pages 274–283. Springer Berlin Heidelberg.
He, Q., Pei, J., Kifer, D., Mitra, P., and Giles, L.
(2010). Context-aware citation recommendation. In
Proceedings of the 19th International Conference on
World Wide Web, WWW ’10, pages 421–430.
Hong, K., Jeon, H., and Jeon, C. (2013a). Advanced person-
alized research paper recommendation system based
on expanded userprofile through semantic analysis.
International Journal of Digital Content Technology
and its Applications(JDCTA), 7(15):67–76.
Hong, K., Jeon, H., and Jeon, C. (2013b). Personalized re-
search paper recommendation system using keyword
extraction based on userprofile. Journal of Conver-
gence Information Technology(JCIT), 8(16):106–116.
Levine, T. R. and Hullett, C. R. (2002). Eta squared,
partial eta squared, and misreporting of effect size
in communication research. Human Communication
Research, 28(4):612–625.
Ma, N., Guan, J., and Zhao, Y. (2008). Bringing pagerank
to the citation analysis. Inf. Process. Manage.,
44(2):800–810.
Nykl, M., Jezek, K., Fiala, D., and Dost
´
al, M. (2014).
Pagerank variants in the evaluation of citation net-
works. J. Informetrics, 8(3):683–692.
Pohl, S., Radlinski, F., and Joachims, T. (2007). Recom-
mending related papers based on digital library access
records. In Proceedings of the 7th ACM/IEEE-CS
Joint Conference on Digital Libraries, JCDL ’07,
pages 417–418, New York, NY, USA. ACM.
Sayyadi, H. and Getoor, L. (2009). Futurerank: Ranking
scientific articles by predicting their future pagerank.
In In Proc. of the 9th SIAM International Conference
on Data Mining, pages 533–544.
Stanovich, K. E. (1986). Matthew effects in reading: Some
consequences of individual differences in the acqui-
sition of literacy. In Reading Research Quarterly,
volume 22.
Sugiyama, K. and Kan, M. (2010). Scholarly paper
recommendation via user’s recent research interests.
In Proceedings of the 2010 Joint International Con-
ference on Digital Libraries, JCDL 2010, Gold Coast,
Queensland, Australia, June 21-25, 2010, pages
29–38.
Sun, Y., Barber, R., Gupta, M., Aggarwal, C., and
Han, J. (2011). Co-author relationship prediction in
heterogeneous bibliographic networks. In Advances
in Social Networks Analysis and Mining (ASONAM),
2011 International Conference on, pages 121–128.
Tang, J., Sun, J., Wang, C., and Yang, Z. (2009).
Social influence analysis in large-scale networks. In
Proceedings of the 15th ACM SIGKDD International
Conference on Knowledge Discovery and Data Min-
ing, KDD ’09, pages 807–816, New York, NY, USA.
ACM.
Tang, J., Zhang, D., and Yao, L. (2007). Social network
extraction of academic researchers. In Data Mining,
2007. ICDM 2007. Seventh IEEE International Con-
ference on, pages 292–301.
Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., and
Su, Z. (2008). Arnetminer: Extraction and mining
of academic social networks. In Proceedings of
the 14th ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining, KDD ’08,
pages 990–998, New York, NY, USA. ACM.
Vellino, A. (2009). Recommending journal articles with
pagerank ratings. In In Recommender Systems 2009.
Vellino, A. (2010). A comparison between usage-based and
citation-based methods for recommending scholarly
research articles. Proceedings of the American Society
for Information Science and Technology, 47(1):1–2.
Wang, C., Han, J., Jia, Y., Tang, J., Zhang, D., Yu, Y., and
Guo, J. (2010). Mining advisor-advisee relationships
from research publication networks. In Proceedings
of the 16th ACM SIGKDD International Conference
on Knowledge Discovery and Data Mining, KDD ’10,
pages 203–212, New York, NY, USA. ACM.
Author’s Paper Similarity Prediction based on the Similarity of Textual References to Visual Features
643