Recommender systems and information extraction for researchers
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Webinar delivered to the Data&Text Mining class from prof. Stella at University of Milano-Bicocca.
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Recommender systems and information extraction for researchers
- 1. Recommender Systems and Information Extraction for researchers Marco Rossetti @ross85 6/11/2015 Mendeley, London
- 2. 2 Outline Recommender Systems and Information Extraction for researchers • What is Data Science • What is Mendeley • Recommender Systems at Mendeley • Information Extraction at Mendeley 06/11/2015
- 3. 3 What is Data Science? Recommender Systems and Information Extraction for researchers06/11/2015
- 4. 4 Why a data scientist? Recommender Systems and Information Extraction for researchers06/11/2015
- 5. 5 Who want a data scientist? Recommender Systems and Information Extraction for researchers06/11/2015
- 6. 6 Who want a data scientist? [2] Recommender Systems and Information Extraction for researchers06/11/2015
- 7. 7 Two main types Recommender Systems and Information Extraction for researchers https://www.quora.com/What-is-data-science/answer/Michael-Hochster 06/11/2015
- 8. 8 Two main types [2] Recommender Systems and Information Extraction for researchers https://www.quora.com/What-is-data-science/answer/Michael-Hochster 06/11/2015
- 9. 9 Skills for Data Science Recommender Systems and Information Extraction for researchers http://businessoverbroadway.com/investigating-data-scientists-their-skills-and-team-makeup 06/11/2015
- 10. 10 What is Mendeley Recommender Systems and Information Extraction for researchers06/11/2015
- 11. 11 Mendeley builds tools to help researchers … [2] Recommender Systems and Information Extraction for researchers Read & Organize Search & Discover Collaborate & Network Experiment & Synthesize 06/11/2015
- 12. 12 Read & Organize Recommender Systems and Information Extraction for researchers Reference management Cite-as-you- write Full-text article search Digitalised annotations 06/11/2015
- 13. 13 Search & Discover Recommender Systems and Information Extraction for researchers Mendeley Suggest Literature Search Related Documents 06/11/2015
- 14. 14 Collaborate & Network Recommender Systems and Information Extraction for researchers Research network Professional research groups 06/11/2015
- 15. 15 Mendeley & Elsevier Recommender Systems and Information Extraction for researchers06/11/2015
- 16. 16 Elsevier Products Recommender Systems and Information Extraction for researchers06/11/2015
- 17. 17 Recommender Systems Recommender Systems and Information Extraction for researchers06/11/2015
- 18. 18 What is a Recommender System? Recommender Systems and Information Extraction for researchers Recommender systems are a subclass of information filtering system that seek to predict the 'rating' or 'preference' that a user would give to an item. [Wikipedia] 06/11/2015
- 19. 19 Why Recommender Systems at Mendeley? Recommender Systems and Information Extraction for researchers 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.” 06/11/2015
- 20. 20 Recommender Systems at Mendeley – Related Documents Recommender Systems and Information Extraction for researchers06/11/2015
- 21. 21 Recommender Systems at Mendeley – Mendeley Suggest Recommender Systems and Information Extraction for researchers https://www.mendeley.com/suggest/ 06/11/2015
- 22. 22 Recommender System Components Recommender Systems and Information Extraction for researchers Algorithms Business Logic and Analytics User Experience Data Sources Algorithms Business Logic & Analytics User Interface 06/11/2015
- 23. 23 Data Sources Recommender Systems and Information Extraction for researchers • 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 • … 06/11/2015
- 24. 24 Algorithms Recommender Systems and Information Extraction for researchers 1. Collaborative filtering User-based If Alice read X, Y, Z and Bob read X, Y, Z and W, we recommend W to Alice + Work well 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) 06/11/2015
- 25. 25 Algorithms [2] Recommender Systems and Information Extraction for researchers 1. Collaborative filtering (still) Matrix factorization + Best CF model in literature - 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 ≈ 06/11/2015
- 26. 26 Algorithms [3] Recommender Systems and Information Extraction for researchers 2. Content-based I read articles about text mining, show me other stuff about text mining + Good for cold users (users without data) - 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 06/11/2015
- 27. 27 Algorithms [4] Recommender Systems and Information Extraction for researchers 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 06/11/2015
- 28. 28 Evaluation Recommender Systems and Information Extraction for researchers • 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 • Online Evaluation computing CTR on logs data • Do offline and online correlate? 06/11/2015
- 29. 29 Business Logic / Analytics Recommender Systems and Information Extraction for researchers • 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 • … 06/11/2015
- 30. 30 User Interface Recommender Systems and Information Extraction for researchers • 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! 06/11/2015
- 31. 31 User Interface [2] Recommender Systems and Information Extraction for researchers • However… – Different kinds of users can have different information needs! – The same user in different contexts can have different information needs! VS 06/11/2015
- 32. 32 User Interface [3] Recommender Systems and Information Extraction for researchers • Solution: different lists! • Provide multiple lists that satisfy different information needs • More likely for a user to find something he is interested in 06/11/2015
- 33. 33 Lesson learned Recommender Systems and Information Extraction for researchers • It’s not about the best algorithm, it’s about the entire user experience! • Easier (if you can) to put together different lists that serve different information needs than to try to satisfy every user with a single list 06/11/2015
- 34. 34 Information Extraction Recommender Systems and Information Extraction for researchers06/11/2015
- 35. 35 Lots of content in an article Recommender Systems and Information Extraction for researchers06/11/2015
- 36. 36 Metadata Extraction Recommender Systems and Information Extraction for researchers • Metadata extraction from PDFs was one of the first features of Mendeley • It makes easy to organize your articles • It powers Mendeley catalog 06/11/2015
- 37. 37 Citation Extraction Recommender Systems and Information Extraction for researchers • Citation extraction from any source and link to the Mendeley catalog • It extracts citable references and a narrative path in the Mendeley environment 06/11/2015
- 38. 38 Machine learning for extraction Recommender Systems and Information Extraction for researchers • Conditional Random Fields (CRF) [1] • We label sequences of tokens yt given feature functions fk(yt, xt) • E.g. ‘yt is AUTHOR and xt-1 is bold’ and ‘yt is AUTHOR and yt-1 is TITLE’ [1] J. Lafferty, A. McCallum and F. Pereira. Conditional random fields: probabilistic models for segmenting and labeling sequence data. In ICML, 2001 Fig. 2.4 in Sutton & McCallum 2011 observations states 06/11/2015
- 39. 39 What cites this work Recommender Systems and Information Extraction for researchers06/11/2015
- 40. 40 What cites this work [2] Recommender Systems and Information Extraction for researchers06/11/2015
- 41. 41 Mendeley Research Maps Recommender Systems and Information Extraction for researchers https://marcorossettiblog.wordpress.com/2015/07/05/mendeley-research-maps/ 06/11/2015
- 42. 42 Thank you Recommender Systems and Information Extraction for researchers06/11/2015