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Research recommendations at Mendeley

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Presentation delivered at the 5th RecSysNL in Amsterdam

Published in: Data & Analytics

Research recommendations at Mendeley

  1. 1. Research recommendations at Mendeley Marco Rossetti Data Scientist @ross85 24/11/2015 Elsevier, Amsterdam
  2. 2. 2 Outline Research recommendations at Mendeley • What is Mendeley • Recommender Systems at Mendeley – Why – Data Sources – Algorithms – Business Logic & Analytics – User Interface 24/11/2015
  3. 3. 3 What is Mendeley Research recommendations at Mendeley24/11/2015
  4. 4. 4 Mendeley builds tools to
 help researchers … Research recommendations at Mendeley Read & Organize Search & Discover Collaborate & Network Experiment & Synthesize 24/11/2015
  5. 5. 5 Read & Organize Research recommendations at Mendeley Reference management Cite-as-you- write Full-text article search Digitalised annotations 24/11/2015
  6. 6. 6 Search & Discover Research recommendations at Mendeley Mendeley Suggest Literature Search Related Documents 24/11/2015
  7. 7. 7 Collaborate & Network Research recommendations at Mendeley Research network Groups 24/11/2015
  8. 8. 8 Mendeley & Elsevier Research recommendations at Mendeley24/11/2015
  9. 9. 9 Recommender Systems Research recommendations at Mendeley24/11/2015
  10. 10. 10 Why Recommender Systems
 at Mendeley? Research recommendations at Mendeley Vision: “To build a personalised research advisor that helps you to organise your work, contextualise it within the global body of research, and connect you with relevant researchers and artifacts.” 24/11/2015
  11. 11. 11 Recommender Systems
 at Mendeley – Related Documents Research recommendations at Mendeley24/11/2015
  12. 12. 12 Recommender Systems
 at Mendeley – Mendeley Suggest Research recommendations at Mendeley https://www.mendeley.com/suggest/ 24/11/2015
  13. 13. 13 Recommender System
 Components Research recommendations at Mendeley Algorithms Business Logic and Analytics User Experience Data Sources Algorithms Business Logic & Analytics User Interface 24/11/2015
  14. 14. 14 • Mendeley – User Libraries • What the users have in their libraries (what they read, what they annotate, what they highlight, what folders they have, etc. etc.) – Articles metadata (title, authors, abstract, keywords, tags, etc. etc.) – Groups • Scopus – Citation network • Science Direct – Logs • … Data Sources Research recommendations at Mendeley24/11/2015
  15. 15. 15 Algorithms Research recommendations at Mendeley 1.  Collaborative filtering User-based If Alice read X, Y, Z and Bob read X, Y, Z and W, we recommend W to Alice + Efficient for us because users << items - Only for users with enough articles in the library Item-based Users who read X also read Y + Item-item similarity matrix is useful to model last n articles read - Expensive in our setting (millions of items) 24/11/2015
  16. 16. 16 Algorithms [2] Research recommendations at Mendeley 1.  Collaborative filtering (still) Matrix factorization + Best CF model in literature - A lot of latent factors, generate recommendations on a catalog of million of items is too slow 1 1 1 1 1 1 ? ? 1 ? 1 ? 1 1 1 1 1 1 1 1 U n x k V k x m X n x m X ≈ 24/11/2015
  17. 17. 17 Algorithms [3] Research recommendations at Mendeley 2.  Content-based I read articles about text mining, show me other stuff about text mining + Good for semi-cold users (users with only a few articles) - Overspecialisation: items recommended are too similar 3.  Popularity/Trending I work in Computer Science, show me popular/trending articles in Computer Science + Perfect for cold users - Non personalised, discipline too broad 24/11/2015
  18. 18. 18 Algorithms [4] Research recommendations at Mendeley 4.  Citation Network Articles similar to articles I cited Articles that cite me Articles from my co-author + Good for some kind of users - Young researchers do not have (enough) publications 24/11/2015
  19. 19. 19 Offline experiments Research recommendations at Mendeley Offline Evaluation of 100+ algorithms variations on an historical dataset • Split data into training and testing based on timestamps: train until day X, try to predict what users will add in the next day/week/month • Computed different metrics to measure different dimensions: • Accuracy (precision, recall, f-score, nDCG, MAP) • Diversity • Recency • Popularity • Consistency • Coverage 24/11/2015
  20. 20. 20 Offline results Research recommendations at Mendeley Warm Users Cold Users 24/11/2015 User Based CF Item Based CF Content Based Citation Network Popularity Trending Content Based Popularity Trending
  21. 21. 21 Business Logic / Analytics Research recommendations at Mendeley • Business put some constraints that could have an impact on the recommendation experience – Don’t show articles outside the user discipline – Show articles only with a minimum readership – Show only recommendations that you can explain (especially for people recommendations, a different matter) • Analytics – Dashboard on the recommender statistics: • Number of recommendations served • Number of users with recommendations • … 24/11/2015
  22. 22. 22 User Interface Research recommendations at Mendeley • Original idea: One list fits all Create a single list with the best recommendations for the user: use advanced methods to take into account every signal and provide what is best for you! 24/11/2015
  23. 23. 23 User Interface [2] Research recommendations at Mendeley • However… – Different kinds of users can have different information needs! – The same user in different contexts can have different information needs! VS 24/11/2015
  24. 24. 24 User Interface [3] Research recommendations at Mendeley • Solution: different lists! • Provide multiple lists that satisfy different information needs • More likely for a user to find something he is interested in 24/11/2015
  25. 25. 25 Online Survey Research recommendations at Mendeley Survey with Mendeley Advisors (pre-launch) 24/11/2015 Based on all the articles Good Bad Popular Good Bad Based on the last article Good Bad Trending Good Bad
  26. 26. 26 Online Statistics Research recommendations at Mendeley Different statistics collected: • overall and list • click on title or
 add to library • different metrics: – # users – CR – CTR 24/11/2015
  27. 27. 27 What’s next Research recommendations at Mendeley • New lists! – Based on your research interests – … • Improve current lists • Researchers you may want to follow 24/11/2015
  28. 28. 28Research recommendations at Mendeley24/11/2015 Thank you!

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