Machine Learning @ Mendeley

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Presentation given at Mendeley's Open Day 2014. A high level description of what machine learning is used for in Mendeley before going a little deeper in user profiling and recommender systems. I am responsible for the first part of the presentation only. Lili Tcheang and Maya Hristakeva created parts II and II respectively.

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<ul><li> 1. Machine Learning@Mendeley Part I: Introduction Presenter Name: Kris Jack (@_krisjack), Lili Tcheang and Maya Hristakeva Presenter Title: Chief Data Scientist, Data Analyst, Senior Data Scientist Date: 01/10/14 </li> <li> 2. What is Machine Learning (ML)? Applications of Machine Learning Spam Detection for Email Personalised Search (e.g. Google, Bing) Speech Recognition Recommender System (e.g. Amazon, Netflix) Definition: Software that improves itself with experience (e.g. exposure to new data) </li> <li> 3. Why is ML Important to Mendeley? Information Extraction Metadata extraction Catalogue: Crowdsourcing Duplicate detection User Profiling Descriptive and Predictive Recommendations Contextualise research Make new discoveries </li> <li> 4. A Closer Look at 2 ML Applications User Profiling: What are our users currently doing with Mendeley? Recommender Systems: How are we helping researchers with their work? </li> <li> 5. Machine Learning@Mendeley Part II: What kind of User are you? User Profiling Presenter Name: Kris Jack (@_krisjack), Lili Tcheang and Maya Hristakeva Presenter Title: Chief Data Scientist, Data Analyst, Senior Data Scientist Date: 01/10/14 </li> <li> 6. Heres What We Did Take 2000 users. Extract 40 activities recorded for those 2000 users. See how those users cluster according to their activities. </li> <li> 7. What were those activities? Social Group invites New followee Posts etc Productivity Reading Writing Search etc </li> <li> 8. Heres how people cluster -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 1.2 1.4 -0.4 0.1 0.6 1.1 1.6 More Productivity Social ProductivityBoth </li> <li> 9. Why is this interesting? Career Progression Student PhD Student Post Doc Professor Social Productivity Both Librarian Other </li> <li> 10. Machine Learning@Mendeley Part III: Recommendations Presenter Name: Kris Jack (@_krisjack), Lili Tcheang and Maya Hristakeva Presenter Title: Chief Data Scientist, Data Analyst, Senior Data Scientist Date: 01/10/14 </li> <li> 11. Recommendations @ Mendeley Mendeley Suggest Related Research Mendeley Digest People Recommender </li> <li> 12. Recommendations @ Mendeley Mendeley Suggest Related Research Mendeley Digest People Recommender </li> <li> 13. Recommendations @ Mendeley Mendeley Suggest Related Research Mendeley Digest People Recommender </li> <li> 14. Recommendations @ Mendeley Mendeley Suggest Related Research Mendeley Digest People Recommender </li> <li> 15. Recommendations @ Mendeley Mendeley Suggest Related Research Mendeley Digest People Recommender your coauthors authors of the paper you are reading (i.e. real- time) your colleagues authors of work that interests you Authors you cited/were cited by Authors who are influential/ trending in your field Authors who your influencers follow </li> <li> 16. Recommendations @ Mendeley Mendeley Suggest Related Research Mendeley Digest People Recommender </li> <li> 17. ML @ Mendeley Information Extraction Metadata extraction Catalogue: Crowdsourcing Duplicate detection User Profiling Descriptive and Predictive Recommendations Contextualise research Make new discoveries </li> <li> 18. Thank you for your time www.mendeley.com </li> </ul>