description of CORE for the EPFL REACT team in Google Hangouts

1. CORE
CO-author REcommendation using network information and interest similarity

2. Overview
• Problem statement
• Data collection & storage
• Calculations
• Technical infrastructure
• Conclusion

3. Problem statement
• Researchers in search of future
cooperation partners
• writing a paper
• writing a project proposal
• finding people with similar
interest

4. Problem statement
• Researchers in search of future
cooperation partners
• writing a paper
• writing a project proposal
• finding people with similar
interest
Whom to choose / ask when you want to work together?http://www.flickr.com/photos/jaygooby/

5. CORE
Similarity (of interest) / homophily (Ibarra, 1992; Lazarsfeld &
Merton, 1954; McPherson, Smith-Lovin, & Cook, 2001; Stahl, 2005)
Influence / Power over information/dissemination flow
(similar to Word-of-Mouth (Money, Gilly, & Graham, 1998; Park & Suh, 2013))

6. Data collection
• data:
• dspace.ou.nl (recommendation)
• Google scholar h-index (visualisation)
• Mendeley hr-index (visualisation)
• storage: MAMP

7. dspace harvester response
• identifier
• timestamp
• title
• creator: authors
• subject: keywords
• description:APA ref,
sponsors
• language
• type: conf. paper, article,
book chapter
<?xml version="1.0" encoding="UTF-8"?>
<OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/
http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd">
<responseDate>2002-02-08T08:55:46Z</responseDate>
<request verb="GetRecord" identifier="oai:arXiv.org:cs/0112017"
metadataPrefix="oai_dc">http://arXiv.org/oai2</request>
<GetRecord>
<record>
<header>
<identifier>oai:arXiv.org:cs/0112017</identifier>
<datestamp>2001-12-14</datestamp>
<setSpec>cs</setSpec>
<setSpec>math</setSpec>
</header>
<metadata>
<oai_dc:dc
xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"
xmlns:dc="http://purl.org/dc/elements/1.1/"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/
http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
<dc:title>Using Structural Metadata to Localize Experience of
Digital Content</dc:title>
<dc:creator>Dushay, Naomi</dc:creator>
<dc:subject>Digital Libraries</dc:subject>
<dc:description>With the increasing technical sophistication of
both information consumers and providers, there is
increasing demand for more meaningful experiences of digital
information. We present a framework that separates digital
object experience, or rendering, from digital object storage
and manipulation, so the rendering can be tailored to
particular communities of users.
</dc:description>
<dc:description>Comment: 23 pages including 2 appendices,
8 figures</dc:description>
<dc:date>2001-12-14</dc:date>
</oai_dc:dc>
</metadata>
</record>
</GetRecord>
</OAI-PMH>

8. Data storage
4.3.2 New data collection
On top of the initial data we also collect data from two different sources.
Illustration 10: Data structure database Sie; red objects are relevant to COCOON CORE project

9. Additional data: h-index
• For each article:
• search google scholar
• scrape citations
• 1000 requests → Captcha
• → switch to another server
• total runtime: 1 hour
• Compute h-index per year

10. Additional data: hr-index
• h-index for Mendeley reads
• Reader Meter

11. Architecture

12. Interest similarity
• Vector space model (Salton,Wang, &Yang, 1975)
• every author has a keyword vector
• per keyword:TF-IDF = term frequency * inverse document
frequency
• boolean TF: 1 if author uses keyword, 0 otherwise
• IDF: all authors / number of times keyword is used by an author
• compute cosine similarity between vectors

13. Betweenness centrality
• requirement: a network
• co-author network

14. Betweenness centrality
• requirement: a network
• co-author network

15. Betweenness centrality
• requirement: a network
• co-author network

16. Betweenness centrality
• requirement: a network
• co-author network

17. Betweenness centrality
• requirement: a network
• co-author network

18. Betweenness centrality
• requirement: a network
• co-author network

19. OUNL co-author network5
Fig. 1. Co-authorship network
X.2.2 Calculations
Sie et al. (accepted)

20. Betweenness centrality
• betweenness = number of times an author is on the shortest
path between two other authors / total number of shortest
paths

21. Betweenness centrality
number of times v is on the shortest path between s and t
number of shortest paths between s and t

22. Betweenness centrality
g(Hendrik) = σMarlies,Erik(Hendrik)/σMarlies,Erik + σMarlies,Denis(Hendrik)/
σMarlies,Denis + σPeter,Erik(Hendrik)/σPeter,Erik + σPeter,Denis(Hendrik)/
σPeter,Denis + σRory,Erik(Hendrik)/σRory,Erik + σRory,Denis(Hendrik)/σRory,Denis
g(Hendrik) = 2/2 + 2/2 + 1/1 + 1/1 + 1/1 + 1/1 = 6
normalization by (N-1)(N-2)/2 gives Cb(Hendrik) = 0.6Marlies
Peter
Rory
Hendrik
Erik
Denis

23. Betweenness centrality
g(Hendrik) = σMarlies,Erik(Hendrik)/σMarlies,Erik + σMarlies,Denis(Hendrik)/
σMarlies,Denis + σPeter,Erik(Hendrik)/σPeter,Erik + σPeter,Denis(Hendrik)/
σPeter,Denis + σRory,Erik(Hendrik)/σRory,Erik + σRory,Denis(Hendrik)/σRory,Denis
g(Hendrik) = 2/2 + 2/2 + 1/1 + 1/1 + 1/1 + 1/1 = 6
normalization by (N-1)(N-2)/2 gives Cb(Hendrik) = 0.6Marlies
Peter
Rory
Hendrik
Erik
Denis
Hendrik is on the edge of his network

24. Problem
• Find a new co-author with:
• similar interest (vector similarity)
• influence (betweenness centrality)

25. GUI

26. GUI
fill out additional keywords

27. GUI
add keyword to user’s vector

28. GUI
adjust sliders to your liking

29. GUI
press the button

30. Recommendation result

31. Author page (1/2)

32. Author page (2/2)

33. Keyword page

34. Usability
• SUS System Usability Scale: 67/100 points
• Q4: no help from a technical person needed
COCOON CORE (questions 4 and 10, Figures 7 and 8), for instance not needing a
technical person to use COCOON CORE (question 4). Also, when looking at the
proportions of responses (Figure 8), participants think that there are few inconsist-
encies in COCOON CORE (question 6) and that COCOON CORE is not unneces-
sarily complex (question 2).
Fig. 7. Median score for each question of the System Usability Scale (SUS)
0"
0,5"
1"
1,5"
2"
2,5"
3"
3,5"
4"
1" 2" 3" 4" 5" 6" 7" 8" 9" 10"
Median'
Questions'

35. Considerations
• Interest similarity: Keyword vector or keyword network?
• average distance between their keywords?
• Wordnet as keyword network
• GUI:
• KISS
• Connect individuals directly
• Performance and scalability:
• graph search depth
• smart indexing
• PHP or JAVA?

36. References
• Ibarra, H. (1992). Homophily and Differential Returns : Sex Differences in Network Structure and Access in an Advertising Firm. Science, 37(3), 422–447.
• Lazarsfeld, P. F., & Merton, R. K. (1954). Friendship as a social process:A substantive and methodological analysis. In M. Berger,T.Abel, & C. H. Page (Eds.),
Freedom and Control in Modern Society (Vol. 18, pp. 18–66).Van Nostrand. Retrieved from http://www.questia.com/PM.qst?a=o&docId=23415760
• McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a Feather: Homophily in Social Networks.Annual Review of Sociology, 27(1), 415–444. doi:
10.1146/annurev.soc.27.1.415
• Money, R. B., Gilly, M. C., & Graham, J. L. (1998). Explorations of National Culture and Word-of-Mouth Referral Behavior in the Purchase of Industrial
Services in the United States and Japan. Journal of Marketing, 62(October), 76–87.
• Park, J. H., & Suh, B. (2013).The impact of influential’s betweenness centraon the WOM effect under the online social networkingservice environment. In
Pacific Asia Conference on Information Systems (PACIS 2013). Jeju Island, Korea:The Korea Society of Management Information Systems.
• Salton, G.,Wong,A., &Yang, C. S. (1975).A vector space model for automatic indexing. Information Retrieval and Language Processing, 18(11), 613–620.
• Sie R. L. L.,Van Engelen, B.J., Bitter-Rijpkema, M., & Sloep, P. B. (accepted). COCOON CORE: CO-author Recommendation based on Betweenness
Centrality and Interest Similarity. SpringerVolume on Recommender Systems for Technology Enhanced Learning: Research Trends & Applications, pp.
• Stahl, G. (2005). Group cognition in computer-assisted collaborative learning. Journal of Computer Assisted Learning, 21(2), 79–90. doi:10.1111/j.
1365-2729.2005.00115.x

37. Networks are everywhere

38. Thank you for your attention!
rory.sie@ou.nl
http://www.open.ou.nl/rse
openrory, maisonpoublon
Rory Sie
openrse
http://nl.linkedin.com/in/rorysie
thebigbangrory.blogspot.com