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Making Mendeley's Research Catalogue More Useful

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Kris Jack
Kris Jack

Presentation given at Workshop on Academic-Industrial Collaborations for Recommender Systems 2013 (http://bit.ly/114XDsE), JCDL'13. A walk through Mendeley as a platform, growing pains involved with engineering at a large scale, the data that we're making publicly available and some demos that have come out of academic collaborations.

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Mendeley’s Research Catalogue: building it, opening it up and making it even more useful for researchers Kris Jack, PhD Chief Data Scientist, @_krisjack
Outline 1. What‘s Mendeley? 2. Under the Bonnet 3. Opening up Data 4. Working with Academia 5. Conclusions
What's Mendeley?
Mendeley‘s not just a reference manager
è  Mendeley is a platform that connects researchers, research data and apps Mendeley Open API
Mendeley Open API research catalogue è  Mendeley is a platform that connects researchers, research data and apps
...organise their research Mendeley provides tools to help users... è  Reference management è  Cite-as-you- write è  Full-text article search è  Digitalised annotations
...organise their research ...collaborate with one another Mendeley provides tools to help users... è  Professional research groups è  Social network è  Annotation sharing
...organise their research ...collaborate with one another ...discover new research Mendeley provides tools to help users... è  Explore crowdsourced research catalogue è  Document statistics è  Personalised article recommendations è  Related research è  Research contact suggestions
...organise their research ...collaborate with one another ...discover new research Mendeley provides tools to help users...
...organise their research ...collaborate with one another ...discover new research Mendeley provides tools to help users...
Social network (>2.4M users) Research catalogue (~85M unique articles) Research groups (~240K groups) Personal libraries (>425M articles) Our community from a data perspective Logging massive set of usage data
Under the Bonnet
Lots of features to build & support è  Reference management è  Cite-as-you- write è  Full-text article search è  Digitalised annotations è  Professional research groups è  Social network è  Annotation sharing è  Explore crowdsourced research catalogue è  Document statistics è  Personalised article recommendations è  Related research è  Research contact suggestions
Lots of features to build & support è  Reference management è  Cite-as-you- write è  Full-text article search è  Digitalised annotations è  Professional research groups è  Social network è  Annotation sharing è  Explore crowdsourced research catalogue è  Document statistics è  Personalised article recommendations è  Related research è  Research contact suggestions
Lots of features to build & support è  Reference management è  Cite-as-you- write è  Full-text article search è  Digitalised annotations è  Professional research groups è  Social network è  Annotation sharing è  Explore crowdsourced research catalogue è  Document statistics è  Personalised article recommendations è  Related research è  Research contact suggestions
Lots of features to build & support features
Lots of features to build & support features Research catalogue (~30M unique articles) Personal libraries (>100M articles)
Lots of features to build & support features Research catalogue (~30M unique articles) Personal libraries (>100M articles) Crowdsourcing (deduplication, metadata aggregation, statistics)
The curse of success •  More articles came •  More users came •  Keeping catalogue data fresh was a burden •  Algorithms relied on global counts •  Iterating over MySQL tables was slow •  Needed to shard tables to grow catalogue •  In short, our backend system didn’t scale
Please try again later
~0.5 million users; the 20 largest user bases: University of Cambridge Stanford University MIT University of Michigan Harvard University University of Oxford Sao Paulo University Imperial College London University of Edinburgh Cornell University University of California at Berkeley RWTH Aachen Columbia University Georgia Tech University of Wisconsin UC San Diego University of California at LA University of Florida University of North Carolina ~30m research articles
~0.5 million users; the 20 largest user bases: University of Cambridge Stanford University MIT University of Michigan Harvard University University of Oxford Sao Paulo University Imperial College London University of Edinburgh Cornell University University of California at Berkeley RWTH Aachen Columbia University Georgia Tech University of Wisconsin UC San Diego University of California at LA University of Florida University of North Carolina ~30m research articles The system started to become slow. How long did it take to generate our daily readership statistics?
~0.5 million users; the 20 largest user bases: University of Cambridge Stanford University MIT University of Michigan Harvard University University of Oxford Sao Paulo University Imperial College London University of Edinburgh Cornell University University of California at Berkeley RWTH Aachen Columbia University Georgia Tech University of Wisconsin UC San Diego University of California at LA University of Florida University of North Carolina ~30m research articles The system started to become slow. How long did it take to generate our daily readership statistics? 23 hours!
We had serious needs •  Build a catalogue based on billions of articles •  Support many features that rely on the catalogue •  Statistics •  Search •  Recommendations •  Sharing •  Data •  Freshness •  Consistency •  Business context •  Agile development (rapid prototyping) •  Cost effective •  Going viral •  Technical debt stacking up
Enter Hadoop What is Hadoop? The Apache Hadoop Project develops open-source software for reliable, scalable, distributed computing www.hadoop.apache.org
Hadoop •  Designed to operate on a cluster of computers •  1…thousands •  Commodity hardware (low cost units) •  Each node offers local computation and storage •  Provides framework for working with big data (beyond petabytes)
New tech stack for backend features Research catalogue (~30M unique articles) Personal libraries (>100M articles) Crowdsourcing (deduplication, metadata aggregation, statistics)
New tech stack for backend features Research catalogue (~30M unique articles) Personal libraries (>100M articles) Crowdsourcing (deduplication, metadata aggregation, statistics) 23 hr computations now took 15 minutes
New tech stack for backend features Research catalogue (~30M unique articles) Personal libraries (>100M articles) Crowdsourcing (deduplication, metadata aggregation, statistics) recommended reading
Mendeley Suggest
Generating recommendations through matrix multiplication This is item-based recommendations as similarity is based on items, not users org.apache.mahout.cf.taste.hadoop.item.RecommenderJob
Running on Amazon's Elastic Map Reduce On demand use and easy to cost
NormalisedAmazonHours No. Good Recommendations/10 0 1K 2K 3K 4K 5K 6K 7K 0 0.5 1 1.5 2 2.5 Costly & Bad Costly & Good Cheap & Bad Cheap & Good 6.5K, 1.5 Orig. item-based 3 Mahout's Performance
NormalisedAmazonHours No. Good Recommendations/10 0 1K 2K 3K 4K 5K 6K 7K 0 0.5 1 1.5 2 2.5 Costly & Bad Costly & Good Cheap & Bad Cheap & Good 6.5K, 1.5 Orig. item-based Cust. item-based è 2.4K, 1.5 3 Mahout's Performance
NormalisedAmazonHours No. Good Recommendations/10 0 1K 2K 3K 4K 5K 6K 7K 0 0.5 1 1.5 2 2.5 Costly & Bad Costly & Good Cheap & Bad Cheap & Good 6.5K, 1.5 Orig. item-based Cust. item-based è 2.4K, 1.5 3 -4.1K (63%) Mahout's Performance
NormalisedAmazonHours No. Good Recommendations/10 0 1K 2K 3K 4K 5K 6K 7K 0 0.5 1 1.5 2 2.5 Costly & Bad Costly & Good Cheap & Bad Cheap & Good 6.5K, 1.5 Orig. item-based Cust. item-based è 2.4K, 1.5 3 Mahout's Performance
NormalisedAmazonHours No. Good Recommendations/10 0 1K 2K 3K 4K 5K 6K 7K 0 0.5 1 1.5 2 2.5 Costly & Bad Costly & Good Cheap & Bad Cheap & Good 6.5K, 1.5 Orig. item-based Cust. item-based è 2.4K, 1.5 Orig. user-based è 1K, 2.5 3 Mahout's Performance
NormalisedAmazonHours No. Good Recommendations/10 0 1K 2K 3K 4K 5K 6K 7K 0 0.5 1 1.5 2 2.5 Costly & Bad Costly & Good Cheap & Bad Cheap & Good 6.5K, 1.5 Orig. item-based Cust. item-based è 2.4K, 1.5 Orig. user-based è 1K, 2.5 3 -1.4K (58%) +1 (67%) Mahout's Performance
NormalisedAmazonHours No. Good Recommendations/10 0 1K 2K 3K 4K 5K 6K 7K 0 0.5 1 1.5 2 2.5 Costly & Bad Costly & Good Cheap & Bad Cheap & Good 6.5K, 1.5 Orig. item-based Cust. item-based è 2.4K, 1.5 Orig. user-based è 1K, 2.5 3 Cust. user-based è 0.3K, 2.5 Mahout's Performance
NormalisedAmazonHours No. Good Recommendations/10 0 1K 2K 3K 4K 5K 6K 7K 0 0.5 1 1.5 2 2.5 Costly & Bad Costly & Good Cheap & Bad Cheap & Good 6.5K, 1.5 Orig. item-based Cust. item-based è 2.4K, 1.5 Orig. user-based è 1K, 2.5 3 Cust. user-based è 0.3K, 2.5 -0.7K (70%) Mahout's Performance -4.1K (63%)
NormalisedAmazonHours No. Good Recommendations/10 0 1K 2K 3K 4K 5K 6K 7K 0 0.5 1 1.5 2 2.5 Costly & Bad Costly & Good Cheap & Bad Cheap & Good 6.5K, 1.5 Orig. item-based Cust. item-based è 2.4K, 1.5 Orig. user-based è 1K, 2.5 3 Cust. user-based è 0.3K, 2.5 -6.2K (95%) Mahout's Performance +1 (67%)
Disclaimer: these advantages have costs •  Migrating to a new system (data consistency) •  Setup costs •  Learn black magic to configure •  Hardware for cluster •  Administrative costs •  High learning curve to administrate Hadoop •  Still an immature technology •  You may need to debug the source code •  Developing against Mahout •  Still needs lots of love
Big data backend features Research catalogue (~30M unique articles) Personal libraries (>100M articles) Crowdsourcing (deduplication, metadata aggregation, statistics)
Opening up Data
Social network (>2.4M users) Research catalogue (~85M unique articles) Research groups (~240K groups) Personal libraries (>425M articles) Our community from a data perspective Logging massive set of usage data
Challenge: Build an application with our data, make science more open. PloS/Mendeley's Binary Battle More details at http://dev.mendeley.com/api-binary-battle/
Challenge: Build off-line system for scientific recommendations with our API and DataTEL data set ScienceRec Challenge 2012 More details at http://2012.recsyschallenge.com/tracks/sciencerec/
Challenge: Build off-line system for scientific recommendations with our API and DataTEL data set ScienceRec Challenge 2012 More details at http://2012.recsyschallenge.com/tracks/sciencerec/
Challenge: Metadata Extraction Challenge The Next Challenge…?
Working with Academia
We have a history of academic collaborations Duration Project 2009-2011 MAKIN’IT 2010-2014 TEAM 2010-2011 DURA 2012-2012 CSL Editor 2012-2014 CODE 2012-2014 ERASM 2013-2015 EEXCESS
Demo CSL Editor http://editor.citationstyles.org/
Demo CODE Mendeley Desktop http://code-research.eu/results
Demo Mendeley Labs http://labs.mendeley.com/
We have a history of academic collaborations Duration Project 2009-2011 MAKIN’IT 2010-2014 TEAM 2010-2011 DURA 2012-2012 CSL Editor 2012-2014 CODE 2012-2014 ERASM 2013-2015 EEXCESS Want to collaborate?
Conclusions
Conclusions è  Mendeley is far more than a reference manager – it‘s a platform that connects researchers, data and apps è  Starting small is good, but be prepared for the cost of scaling up è  We‘re opening up our data for you to build apps on our platform è  We‘re always keen to collaborate with academic groups Kris Jack, PhD Chief Data Scientist, @_krisjack

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Making Mendeley's Research Catalogue More Useful

  • 1. Mendeley’s Research Catalogue: building it, opening it up and making it even more useful for researchers Kris Jack, PhD Chief Data Scientist, @_krisjack
  • 2. Outline 1. What‘s Mendeley? 2. Under the Bonnet 3. Opening up Data 4. Working with Academia 5. Conclusions
  • 4. Mendeley‘s not just a reference manager
  • 5. è  Mendeley is a platform that connects researchers, research data and apps Mendeley Open API
  • 6. Mendeley Open API research catalogue è  Mendeley is a platform that connects researchers, research data and apps
  • 7. ...organise their research Mendeley provides tools to help users... è  Reference management è  Cite-as-you- write è  Full-text article search è  Digitalised annotations
  • 8. ...organise their research ...collaborate with one another Mendeley provides tools to help users... è  Professional research groups è  Social network è  Annotation sharing
  • 9. ...organise their research ...collaborate with one another ...discover new research Mendeley provides tools to help users... è  Explore crowdsourced research catalogue è  Document statistics è  Personalised article recommendations è  Related research è  Research contact suggestions
  • 10. ...organise their research ...collaborate with one another ...discover new research Mendeley provides tools to help users...
  • 11. ...organise their research ...collaborate with one another ...discover new research Mendeley provides tools to help users...
  • 12. Social network (>2.4M users) Research catalogue (~85M unique articles) Research groups (~240K groups) Personal libraries (>425M articles) Our community from a data perspective Logging massive set of usage data
  • 14. Lots of features to build & support è  Reference management è  Cite-as-you- write è  Full-text article search è  Digitalised annotations è  Professional research groups è  Social network è  Annotation sharing è  Explore crowdsourced research catalogue è  Document statistics è  Personalised article recommendations è  Related research è  Research contact suggestions
  • 15. Lots of features to build & support è  Reference management è  Cite-as-you- write è  Full-text article search è  Digitalised annotations è  Professional research groups è  Social network è  Annotation sharing è  Explore crowdsourced research catalogue è  Document statistics è  Personalised article recommendations è  Related research è  Research contact suggestions
  • 16. Lots of features to build & support è  Reference management è  Cite-as-you- write è  Full-text article search è  Digitalised annotations è  Professional research groups è  Social network è  Annotation sharing è  Explore crowdsourced research catalogue è  Document statistics è  Personalised article recommendations è  Related research è  Research contact suggestions
  • 17. Lots of features to build & support features
  • 18. Lots of features to build & support features Research catalogue (~30M unique articles) Personal libraries (>100M articles)
  • 19. Lots of features to build & support features Research catalogue (~30M unique articles) Personal libraries (>100M articles) Crowdsourcing (deduplication, metadata aggregation, statistics)
  • 20.
  • 21.
  • 22.
  • 23.
  • 24. The curse of success •  More articles came •  More users came •  Keeping catalogue data fresh was a burden •  Algorithms relied on global counts •  Iterating over MySQL tables was slow •  Needed to shard tables to grow catalogue •  In short, our backend system didn’t scale
  • 26. ~0.5 million users; the 20 largest user bases: University of Cambridge Stanford University MIT University of Michigan Harvard University University of Oxford Sao Paulo University Imperial College London University of Edinburgh Cornell University University of California at Berkeley RWTH Aachen Columbia University Georgia Tech University of Wisconsin UC San Diego University of California at LA University of Florida University of North Carolina ~30m research articles
  • 27. ~0.5 million users; the 20 largest user bases: University of Cambridge Stanford University MIT University of Michigan Harvard University University of Oxford Sao Paulo University Imperial College London University of Edinburgh Cornell University University of California at Berkeley RWTH Aachen Columbia University Georgia Tech University of Wisconsin UC San Diego University of California at LA University of Florida University of North Carolina ~30m research articles The system started to become slow. How long did it take to generate our daily readership statistics?
  • 28. ~0.5 million users; the 20 largest user bases: University of Cambridge Stanford University MIT University of Michigan Harvard University University of Oxford Sao Paulo University Imperial College London University of Edinburgh Cornell University University of California at Berkeley RWTH Aachen Columbia University Georgia Tech University of Wisconsin UC San Diego University of California at LA University of Florida University of North Carolina ~30m research articles The system started to become slow. How long did it take to generate our daily readership statistics? 23 hours!
  • 29. We had serious needs •  Build a catalogue based on billions of articles •  Support many features that rely on the catalogue •  Statistics •  Search •  Recommendations •  Sharing •  Data •  Freshness •  Consistency •  Business context •  Agile development (rapid prototyping) •  Cost effective •  Going viral •  Technical debt stacking up
  • 30. Enter Hadoop What is Hadoop? The Apache Hadoop Project develops open-source software for reliable, scalable, distributed computing www.hadoop.apache.org
  • 31. Hadoop •  Designed to operate on a cluster of computers •  1…thousands •  Commodity hardware (low cost units) •  Each node offers local computation and storage •  Provides framework for working with big data (beyond petabytes)
  • 32. New tech stack for backend features Research catalogue (~30M unique articles) Personal libraries (>100M articles) Crowdsourcing (deduplication, metadata aggregation, statistics)
  • 33. New tech stack for backend features Research catalogue (~30M unique articles) Personal libraries (>100M articles) Crowdsourcing (deduplication, metadata aggregation, statistics) 23 hr computations now took 15 minutes
  • 34. New tech stack for backend features Research catalogue (~30M unique articles) Personal libraries (>100M articles) Crowdsourcing (deduplication, metadata aggregation, statistics) recommended reading
  • 36.
  • 37. Generating recommendations through matrix multiplication This is item-based recommendations as similarity is based on items, not users org.apache.mahout.cf.taste.hadoop.item.RecommenderJob
  • 38. Running on Amazon's Elastic Map Reduce On demand use and easy to cost
  • 39. NormalisedAmazonHours No. Good Recommendations/10 0 1K 2K 3K 4K 5K 6K 7K 0 0.5 1 1.5 2 2.5 Costly & Bad Costly & Good Cheap & Bad Cheap & Good 6.5K, 1.5 Orig. item-based 3 Mahout's Performance
  • 40. NormalisedAmazonHours No. Good Recommendations/10 0 1K 2K 3K 4K 5K 6K 7K 0 0.5 1 1.5 2 2.5 Costly & Bad Costly & Good Cheap & Bad Cheap & Good 6.5K, 1.5 Orig. item-based Cust. item-based è 2.4K, 1.5 3 Mahout's Performance
  • 41. NormalisedAmazonHours No. Good Recommendations/10 0 1K 2K 3K 4K 5K 6K 7K 0 0.5 1 1.5 2 2.5 Costly & Bad Costly & Good Cheap & Bad Cheap & Good 6.5K, 1.5 Orig. item-based Cust. item-based è 2.4K, 1.5 3 -4.1K (63%) Mahout's Performance
  • 42. NormalisedAmazonHours No. Good Recommendations/10 0 1K 2K 3K 4K 5K 6K 7K 0 0.5 1 1.5 2 2.5 Costly & Bad Costly & Good Cheap & Bad Cheap & Good 6.5K, 1.5 Orig. item-based Cust. item-based è 2.4K, 1.5 3 Mahout's Performance
  • 43. NormalisedAmazonHours No. Good Recommendations/10 0 1K 2K 3K 4K 5K 6K 7K 0 0.5 1 1.5 2 2.5 Costly & Bad Costly & Good Cheap & Bad Cheap & Good 6.5K, 1.5 Orig. item-based Cust. item-based è 2.4K, 1.5 Orig. user-based è 1K, 2.5 3 Mahout's Performance
  • 44. NormalisedAmazonHours No. Good Recommendations/10 0 1K 2K 3K 4K 5K 6K 7K 0 0.5 1 1.5 2 2.5 Costly & Bad Costly & Good Cheap & Bad Cheap & Good 6.5K, 1.5 Orig. item-based Cust. item-based è 2.4K, 1.5 Orig. user-based è 1K, 2.5 3 -1.4K (58%) +1 (67%) Mahout's Performance
  • 45. NormalisedAmazonHours No. Good Recommendations/10 0 1K 2K 3K 4K 5K 6K 7K 0 0.5 1 1.5 2 2.5 Costly & Bad Costly & Good Cheap & Bad Cheap & Good 6.5K, 1.5 Orig. item-based Cust. item-based è 2.4K, 1.5 Orig. user-based è 1K, 2.5 3 Cust. user-based è 0.3K, 2.5 Mahout's Performance
  • 46. NormalisedAmazonHours No. Good Recommendations/10 0 1K 2K 3K 4K 5K 6K 7K 0 0.5 1 1.5 2 2.5 Costly & Bad Costly & Good Cheap & Bad Cheap & Good 6.5K, 1.5 Orig. item-based Cust. item-based è 2.4K, 1.5 Orig. user-based è 1K, 2.5 3 Cust. user-based è 0.3K, 2.5 -0.7K (70%) Mahout's Performance -4.1K (63%)
  • 47. NormalisedAmazonHours No. Good Recommendations/10 0 1K 2K 3K 4K 5K 6K 7K 0 0.5 1 1.5 2 2.5 Costly & Bad Costly & Good Cheap & Bad Cheap & Good 6.5K, 1.5 Orig. item-based Cust. item-based è 2.4K, 1.5 Orig. user-based è 1K, 2.5 3 Cust. user-based è 0.3K, 2.5 -6.2K (95%) Mahout's Performance +1 (67%)
  • 48. Disclaimer: these advantages have costs •  Migrating to a new system (data consistency) •  Setup costs •  Learn black magic to configure •  Hardware for cluster •  Administrative costs •  High learning curve to administrate Hadoop •  Still an immature technology •  You may need to debug the source code •  Developing against Mahout •  Still needs lots of love
  • 49. Big data backend features Research catalogue (~30M unique articles) Personal libraries (>100M articles) Crowdsourcing (deduplication, metadata aggregation, statistics)
  • 51. Social network (>2.4M users) Research catalogue (~85M unique articles) Research groups (~240K groups) Personal libraries (>425M articles) Our community from a data perspective Logging massive set of usage data
  • 52.
  • 53.
  • 54.
  • 55. Challenge: Build an application with our data, make science more open. PloS/Mendeley's Binary Battle More details at http://dev.mendeley.com/api-binary-battle/
  • 56.
  • 57. Challenge: Build off-line system for scientific recommendations with our API and DataTEL data set ScienceRec Challenge 2012 More details at http://2012.recsyschallenge.com/tracks/sciencerec/
  • 58. Challenge: Build off-line system for scientific recommendations with our API and DataTEL data set ScienceRec Challenge 2012 More details at http://2012.recsyschallenge.com/tracks/sciencerec/
  • 59. Challenge: Metadata Extraction Challenge The Next Challenge…?
  • 61. We have a history of academic collaborations Duration Project 2009-2011 MAKIN’IT 2010-2014 TEAM 2010-2011 DURA 2012-2012 CSL Editor 2012-2014 CODE 2012-2014 ERASM 2013-2015 EEXCESS
  • 65. We have a history of academic collaborations Duration Project 2009-2011 MAKIN’IT 2010-2014 TEAM 2010-2011 DURA 2012-2012 CSL Editor 2012-2014 CODE 2012-2014 ERASM 2013-2015 EEXCESS Want to collaborate?
  • 67. Conclusions è  Mendeley is far more than a reference manager – it‘s a platform that connects researchers, data and apps è  Starting small is good, but be prepared for the cost of scaling up è  We‘re opening up our data for you to build apps on our platform è  We‘re always keen to collaborate with academic groups Kris Jack, PhD Chief Data Scientist, @_krisjack