This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version ofRecord and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-030-64452-9_36[Practitioners paper] A Novel Researcher Search
System Based on Research Content Similarity
and Geographic Information⋆
Tetsuya Takahashi, Koya Tango, Yuto Chikazawa, andMarie Katsurai[0000−0003−4899−2427]
Doshisha University, 1-3 Tatara Miyakodani, Kyotanabe-shi, Kyoto, Japan{takahashi, katsurai}@mm.doshisha.ac.jp

Abstract. Collaborative research is becoming increasingly importantbecause it yields effective results and helps difficult research projects runsmoothly. Previous studies have proposed many kinds of collaborator
recommendation methods based on research features, such as specialty
fields. However, few studies have constructed systems in which users
can discover experts who have similar research interests using recommendation techniques. This paper proposes a novel researcher searchsystem where users can efficiently discover potential candidates whose
work locations are near theirs. Researchers are visualized on a map byour proposed system and users can use researcher’s names and research
keywords to narrow down the search. Specifically, given a researcher’sname as a query, the system displays its relevant individuals based oneither one of the following measures among researchers: research content similarity or collaborative relationship similarity. Our experiments
demonstrated that recommendation results of these two similarity measures are minimally overlapped one another, indicating that our system
could potentially help researchers discover collaborator candidates.
Keywords: researcher search system · collaborator recommendation ·researcher similarity · academic database analysis

1Introduction

Complex research project can be conducted effectively through collaborative research. Several studies have explored the relationship between collaboration andproductivity. For example, Lee and Bozeman [11] investigated how the numberof collaborators has influenced journal publication. Abramo et al. [1] analyzed
the correlation among several types of collaborations, including interdisciplinary
research, extramural collaborative research, industry-academia collaborative research, and their achievements, to assess the correlation between scientific productivity and collaboration intensity. Lopes et al. [13] proposed a method for⋆This research was partly supported by JST ACT-I (Grant number: JPMJPR18UC),
JST ACT-X (Grant number: JPMJAX1909), and JSPS KAKENHI (Grant number:20H04484).

2T. Takahashi et al.

ranking research quality and found that authors of high-ranking research collaborated more. In the field of scholarly data mining, several collaborator recommendation methods have been proposed [14, 3, 10, 15, 5, 12, 2, 9, 16]. Most conventional methods defined the researcher similarity using bibliographic analysis,
such as the closeness of existing relationships and correlation between research
fields. However, few studies constructed systems in which users could discoverresearchers based on the existing recommendation methods.
Here, we focus on the recent work [16], which demonstrated that including thelocations of researchers’ affiliations improved research candidate recommender’sperformance. Inspired from this work, this study proposes a novel researcher
search system based on research content similarity and geographic information
to promote domestic collaboration opportunities. The proposed system allows
users to search for potential collaborator candidates using researchers’ names
and research keywords. Subsequently, users can then filter results based on (i)researchers whose published works feature at least one of the keywords, (ii) existing collaborative partnerships among researchers when searching a researcher’sname, and (iii) researchers whose interests or collaborative partnerships similar
to the query researcher’s those. In particular, in the third function, users can use
the research content or existing collaborative relationships as features to calculate the similarity among researchers. Search results are displayed on a map ofJapan using yellow pins. When a user clicks a pin, our system displays the researcher’s information, including their specializations and past research projects.
Our system enables users to discover researchers with desired specializations
from their neighboring area, which helps encourage collaboration and discussion
between researchers and local research institutions. We constructed the systemusing the Database of Grants-in-Aid for Scientific Research (KAKEN)1 . Our
experiments showed that there is little overlap between researchers found using
content similarity and those in existing collaboration relationships, indicatingthat the proposed method can effectively help users to find potential collaborator candidates using these two different similarity measures.

2

Proposed System

2.1

System overview

The proposed system’s architecture is shown in Fig. 1. Researcher similarity isregularly analyzed on a local computer, and the results are stored in a databaseon the cloud. To fulfill an API request from a client, the server cuts down a large
network to extract a small network. By processing response sent from the serverto the client, users can see results interactively. The client’s home screen is shownin Fig. 2. When users click the search button, a search query field appears, as
shown in Fig. 3. Users can use search queries appearing as candidates for thequery. Clicking a yellow pin displays that researcher’s details (Fig. 4). Users havethe option to have the pins displayed on an aerial photo (as in Fig. 4) or on a1

https://kaken.nii.ac.jp/

A Novel Researcher Search System3Fig. 1. The proposed system’s architecture.

Fig. 2. The proposed system’s home screen.

street map (as in Fig. 5). Hence, local information, such as train stations near
the researcher’s office, can be confirmed.
Users can filter search results by checking corresponding boxes, allowing
them to tailor results based on (i) researchers whose published works feature
at least one of the keywords used, (ii) existing collaborative partnerships among
researchers when searching a researcher’s name, and (iii) researchers whose in-4

T. Takahashi et al.

Fig. 3. The proposed system’s search interface.

Fig. 4. Detail screen of a researcher.

A Novel Researcher Search System

5Fig. 5. Visualization by the street map mode.

terests or collaborative partnerships similar to the query researcher’s those. Section 2.2 describes how to construct a dataset to implement the first and thesecond functions, while Section 2.3 describes two similarity measures to realize
the third function.
2.2

Dataset construction and basic searchThis subsection describes our system’s dataset that consists of researcher information (i.e., research projects, work location information, and collaborative
relationships). In this study, we used KAKEN to construct the researcher searchsystem. KAKEN is the Database of Grants-in-Aid for Scientific Research projects
granted by the Ministry of Education, Culture, Sports, Science and Technology(MEXT) and the Japan Society for the Promotion of Science (JSPS). All of thedatabase’s research reports cover all research fields. Compared to other academic
databases, it is better because it has a lower field deviation and all data can besearched using the same form. KAKEN assigns unique numbers to researchers,
which are linked to their research projects, which display project names, principal investigators, co-investigators, research institutions, research fields, research
keywords, summaries, and corresponding registered researchers’ achievements.
In this paper, we constructed the dataset using 911,724 research projects and259,509 registered researchers.
Geographic information: The proposed system displays institutions eachresearcher belong to using information based on latest research projects. Insti-6T. Takahashi et al.

Fig. 6. A screenshot of the window for visualizing existing collaborative relationships
among researchers.

tution name registered in KAKEN’s projects had spelling inconsistencies, which
we corrected manually. Work location information (i.e., latitude and longitude)
was obtained using Google Maps API 2 , which is visualized on a map of Japan.
Research keywords: In KAKEN, each researcher is assigned keywords
based on the contents of their research. Users can use these keywords when performing searches. Researchers containing keywords corresponding to the search
query are displayed on the map. Through this, users can find researchers who
work in specific fields.
Existing collaborative partnerships: We constructed a network whosenodes were represented by researchers and whose edges were represented bycollaborative relationships in KAKEN projects. When users search a researcher’sname, the researcher’s collaborative relationships are also displayed on the map.
Specifically, the system obtains the network associated with a researcher ID fromthe server and displays the collaborative relationships, which enables users tofind experts who actually work with the query researcher. Figure 6 shows anexample of the relationships between researchers.
2.3

Computing researcher similarity and displaying potential
collaborators

To search potential collaborators, users can choose the similarity of collaborative
relationships or the similarity based on the contents of researcher’s projects.
2

https://cloud.google.com/maps-platform/

A Novel Researcher Search System

7Fig. 7. The process of creating a vector that represents research content.

First, let Rr1 , Rr2 be two sets of research collaborators with each researcher r1 ,r2 . Let the similarity of collaborative relationships between two researchers berepresented by the Jaccard index as follows:
sim1(r1 , r2 ) =|Rr1 ∩ Rr2 |.
|Rr1 ∪ Rr2 |(1)

When there is no overlap between research collaborators, sim1 is 0.
Next, we compute the similarity between researchers based on the contents oftheir research. Each research project is considered to be a text data that reflects
researcher’s interests because each of them is influenced by each contributing
researcher’s achievements. Therefore, we computed the TF-IDF vector using thetitles of research projects, titles of academic papers registered as achievement,
research keywords, and the summary, as shown in Fig. 7.
Although the word embedding can be used to vectorize documents (as whichwas used in the related work [8]), we decided to use the TF-IDF vector toclearly understand coincidence of research interest in the proposed system. Howto effectively combine these different word features will be discussed in our futurework. Let vec1 and vec2 be the TF-IDF vectors calculated for researchers r1 andr2 . We calculated the similarity based on the contents of their research using thecosine similarity:
sim2 = cosine(vec1, vec2).

(2)

8T. Takahashi et al.

The sim2 of 1 means the contents of their research is exactly the same.
In general, it is not realistic to compute similarity between over 100,000researchers in any combination because it requires much calculation cost. Therefore, we used FAISS [7], which includes the nearest-neighbor search, to perform
quick computation. FAISS reduces the dimensionality of a vector through product quantization [6, 4] and performs an approximate nearest-neighbor search.
FAISS previously divided a set of vectors for search into Voronoı̈ regions toimprove search speed and specify search scope. In this study, there were 100
Voronoı̈, and the search scope was 10. Thus, 500 candidates for collaborative
research were computed and saved in the database.

3

Evaluation Experiment

To show that the two methods for computing similarity described in 2.3 wereuseful for discovering potential collaborators, we determined the number of researchers yielded in the search results based on research contents. Specifically,
we computed the overlap ratio of search results overlap as follows:
overlap(sim1, sim2) =|Ssim1 ∩ Ssim2 |,|Ssim1 |(3)

where Ssim denotes a set of researchers based on similarity sim. The lower
overlap(sim1, sim2) is, the more novel researchers excluded based on similarity
sim2 are discovered using similarity sim1. In this experiment, 100 researchers
were chosen at random and the overlap ratio was computed for all pairs. As aresult, the mean value of overlap(sim1, sim2) is 0.199. Because the value is small,
we demonstrated that it is possible to present potential collaborators that users
cannot discover through existing relationships via switching these two similarity
functions.

4

Conclusions and Future Work

This paper presented a novel researcher search system based on research content
similarity and geographic information. In the proposed system, users can freely
search for researchers in Japan using their names and research keywords. Specifically, we implemented three filters: researchers whose published works feature
at least one of the keywords used, existing collaborative partnerships among researchers when searching a researcher’s name, and researchers whose work or
collaborative partnerships similar to the query researcher’s those. It is expectedthat the proposed system will facilitate research collaboration and discussion
among researchers and users that work near one another. In our future work, wewill qualitatively evaluate the system’s usability and consider more methods tocompute similarity.

A Novel Researcher Search System

9References
1. Abramo, G., D’Angelo, C.A., Di Costa, F.: Research collaboration and productivity: is there correlation? Higher Education 57(2), 155–171 (2009)
2. Araki, M., Katsurai, M., Ohmukai, I., Takeda, H.: Interdisciplinary Collaborator
Recommendation Based on Research Content Similarity. IEICE Transactions onInformation and Systems 100(4), 785–792 (2017)
3. Chen, H.H., Gou, L., Zhang, X., Giles, C.L.: CollabSeer: A Search Engine for Collaboration Discovery. In: Proceedings of the 11th Annual International ACM/IEEE
Joint Conference on Digital Libraries. pp. 231–240. ACM (2011)
4. Ge, T., He, K., Ke, Q., Sun, J.: Optimized Product Quantization for Approximate
Nearest Neighbor Search. In: Proceedings of the IEEE Conference on ComputerVision and Pattern Recognition. pp. 2946–2953 (2013)
5. Guo, Y., Chen, X.: Cross-domain Scientific Collaborations Prediction with Citation Information. In: Computer Software and Applications Conference Workshops(COMPSACW), 2014 IEEE 38th International. pp. 229–233. IEEE (2014)
6. Jegou, H., Douze, M., Schmid, C.: Product quantization for nearest neighborsearch. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(1),117–128 (2010)
7. Johnson, J., Douze, M., Jégou, H.: Billion-scale similarity search with GPUs. arXiv
preprint arXiv:1702.08734 (2017)8. Kawamura, T., Watanabe, K., Matsumoto, N., Egami, S.: Mapping Science Based on Research Content Similarity (07 2018).
https://doi.org/10.5772/intechopen.77067
9. Kong, X., Jiang, H., Wang, W., Bekele, T.M., Xu, Z., Wang, M.: Exploring dynamic research interest and academic influence for scientific collaborator recommendation. Scientometrics 113(1), 369–385 (2017)
10. Lee, D.H., Brusilovsky, P., Schleyer, T.: Recommending Collaborators using SocialFeatures and MeSH Terms. Proceedings of the Association for Information Scienceand Technology 48(1), 1–10 (2011)
11. Lee, S., Bozeman, B.: The Impact of Research Collaboration on Scientific Productivity. Social Studies of Science 35(5), 673–702 (2005)
12. Li, J., Xia, F., Wang, W., Chen, Z., Asabere, N.Y., Jiang, H.: ACRec: A Coauthorship based Random Walk Model for Academic Collaboration Recommendation. In: Proceedings of the 23rd International Conference on World Wide Web.
pp. 1209–1214. ACM (2014)
13. Lopes, G., Moro, M., Silva, R., Barbosa, E., Palazzo Moreira de Oliveira, J.: Ranking Strategy for Graduate Programs Evaluation (01 2011)
14. Lopes, G.R., Moro, M.M., Wives, L.K., de Oliveira, J.P.M.: Collaboration Recommendation on Academic Social Networks. In: Advances in Conceptual Modeling –Applications and Challenges. pp. 190–199. Springer Berlin Heidelberg (2010)
15. Tang, J., Wu, S., Sun, J., Su, H.: Cross-domain Collaboration Recommendation.
In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge
Discovery and Data Mining. pp. 1285–1293. ACM (2012)
16. Zhang, Q., Mao, R., Li, R.: Spatial–temporal restricted supervised learning forcollaboration recommendation. Scientometrics 119(3), 1497–1517 (04 2019)