Final project for the Science to Data Science school in London (Aug. 2014) where we worked with company RefME to build the prototype of their recommendation engine.

1. Building a Library Recommendation Engine
www.refme.com
@getrefme
Martina Pugliese
Daniel Pape
Richard Hanson

2. What is RefME?
• award-winning referencing tool!
• synchronised website + mobile app!
• easy to use!
• barcode scanning!
• many reference types!
• import from several formats!
• export in 1000s of styles
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3. Who uses RefME?
• 100 000s users!
• mainly undergraduate students!
• writing essays, theses, building
bibliographies!
• Other tools focus on academic
market!
• RefME is much more general
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4. Our Task
4
RefME wants to broaden the appeal of their product by
offering a tool to build better bibliographies.
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5. Our Task
4
RefME wants to broaden the appeal of their product by
offering a tool to build better bibliographies.
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“take the search out of research”

6. Our Task
Task: Develop a recommendation system!
• suggest relevant references to users!
• general enough to handle any reference type
4
RefME wants to broaden the appeal of their product by
offering a tool to build better bibliographies.
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“take the search out of research”

7. The Data
• small subset of data-base!
• 21 000 projects with 120 000+ references!
• references are user-generated and associated via projects!
• pre-processing: sort/unify identifiers (isbn, doi, url) + retrieve
subject keywords
Recommendation!
Engine Project ID Reference IDs
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8. Collaborative Filtering Content-based Recommendations
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Our Recommendation Engine
“ask your friends” “look at content information”

9. Collaborative Filtering
Project Based
similar projects
nearest neighbours
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10. Project Based Reference Based
similar projects
nearest neighbours
interaction patterns
co-occurrence
Collaborative Filtering
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11. Collaborative Filtering
Project Based Reference Based
similar projects
nearest neighbours
interaction patterns
co-occurrence
multiple similarity measures
for robust results
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12. Collaborative Filtering
Project Based Reference Based
similar projects
nearest neighbours
interaction patterns
co-occurrence
multiple similarity measures
for robust results
recommendations
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13. Collaborative Filtering Content-based Recommendations
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14. Content-based Recommendations
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15. Content-based Recommendations
no references
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16. Content-based Recommendations
no references
project title
compare titles with NLP
retrieve subjects in matching
projects
extract references based on
subject frequency
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17. Content-based Recommendations
no references with references
project title
compare titles with NLP
retrieve subjects in matching
projects
extract references based on
subject frequency
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18. Content-based Recommendations
no references with references
project title subject keywords
compare titles with NLP
retrieve subjects in matching
projects
extract references based on
subject frequency
measure similarity
extract most similar references
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19. Benefits of two approaches
Collaborative Filtering Content-based Recommendations
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independent of item frequency
good for common topics
good for items with sufficient
frequency

20. Project: “Media Research”
• Relocating Television: Television in the Digital Context
• Transmedia Television: Audiences, New Media and Daily Life
• Television and Its Audience
• Television Goes Digital. The Economics of Information
Communication and Entertainment: The Impacts of Digital
Technology in the 21st Century.
OUTPUT INPUT 11 / 13
• Grown Up Digital How the Net Generation is Changing Your World
• Future Minds: How the Digital Age is Changing Our Minds Why
This Matters and What We Can Do About It
• The Television Studies Book
• Television and Everyday Life

21. Project: “Group Presentation”
• Person to Person: Ways of Communicating
• The Psychology of Interpersonal Perception
• Interpersonal Communication
• Body Movement and Interpersonal Communication
• Intergroup Behaviour
OUTPUT INPUT 12 / 13
• The Social Psychology of Everyday
• Interviews Made Easy How to Get the Psychological Advantage
• Groups at Work: Theory and Research
• Theories of Human Communication (with InfoTrac)

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Summary
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RefME wants to broaden the appeal of their product by
offering a tool to build better bibliographies.
Developed recommendation system!
• yields good results for any reference type!
• will be incorporated as premium feature soon

23. www.refme.com
@getrefme
Martina Pugliese
Daniel Pape
Richard Hanson

24. Used Tools
• Python NLTK libraries!
• openlibrary.org API!
• crossref.org API!
• mahout.apache.org!
• R, Java