A Recommender Story - Improving Backend Data Quality While Reducing Costs

A recommender story: improving backend data quality while reducing costsnInformation overload is one of the biggest challenges academics face on a daily basis while finding the right knowledge to advance science. With around 7k research articles being published every day, how do you find the right ones?

Elsevier is a global information analytics business that helps institutions and professionals advance healthcare, open science and improve performance. With many data sources and signals being available, data science and big data engineering provide the perfect opportunity to deliver more value to researchers.

Here we will focus on Mendeley, an open (free of charge) academic content platform to help researchers discover new information via functionalities such as a crowd sourced collection of academic related documents (Catalogue) and various personalized recommender systems. MendeleySuggest, the recommender system, helps millions of researchers worldwide to find documents and people relevant to their research field, they did not yet know exist. The personalised recommenders are powered by Mendeley Catalogue, clustering 2 billion records correctly into canonical records, state of the art algorithms and big data solutions (e.g. Spark).

In the past few years, we noticed that with our content growth, quality of the canonical records started drifting due to scalability issues. As a result, we faced clustering accuracy problems and, in turn, impacting also the recommenders. In this talk we will highlight how we rearchitected the fabrication of Mendeley Catalogue to improve its scalability and accuracy. In addition, we will show how the migration from Hadoop Map Reduce to Spark has helped us reduce costs as well as improving maintainability.

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1.A recommender story: Improving backend data quality while reducing costs Jacques Doux, Elsevier #UnifiedDataAnalytics #SparkAISummit

2.Elsevier – in a nutshel We are a Data and Analytics Company delivering solutions for Science and Health. • Modern Elsevier Born in 1880 in Amsterdam https://www.elsevier.com/about/history • The biggest Academic publisher 38k books – 3000 journals (~25% of ever cited content) • First publisher to have provided electronic version of its content From ADONIS (1979) to ScienceDirect (1997) and now hosting ~17M full text documents • Empowering decision support : https://www.elsevier.com/solutions − Abstract and indexing − Research & Data management tools − Research evaluation and showcasing tools − Adaptative learning for health professionals − Clinical decision support − Discovery sandbox to combine Elsevier high quality data with your own proprietary data 2

3. endeley helps academics stay on top • By providing solutions for − References management − Academic & Research networking − Managing Datasets − Finding academic job opportunity − Finding Funding opportunity • Powered by − Data − Search and Discovery tools 3

4.Mendeley Suggest (MS) Desktop app – Mobile – Web – emails 6.8M emails sent weekly with 3 recommendations https://impactrs19.github.io/papers/paper5.pdf 4

5.How does Mendeley Suggest work • Custom implementation of user based collaborative filtering • Significance Weighting • Time Decay • Impression Discounting • Dithering • Data… lots of it • Content records (Catalogue) • User profiles • Ambient data https://doi.org/10.1142/9789813275355_0018 5

6.Issues with catalogue impacts dependent systems Under-merged Relevant, but already added it a while back Very “focused” recommendations ! Many obvious duplicates creeping up search result… … splitting metrics apart impacting relevance ranking 6

7.How it’s made: Catalogue Evolution of document records in Mendeley catalogue Start project 2,000 M # document records publication of 1,500 M old deduplication algorithm 1,000 M old algorithm something inception needs to be done public birth of 500 M >= 24h to run Mendeley acquisition by too many visible Elsevier issues 0M 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 year of addition https://doi.org/10.1108/PROG-02-2015-0021 7

8.Redesign without breaking: Goals 1. Improve overall user experience Better document disambiguation : better classifier 2. Improve scalability and processing speed Need to work for > 2B records and faster than it currently does 3. Improve code maintainability and ease of evolution Migrate from Hadoop MR in Java to Spark + Scala 8

9. Improving the classifier: are those duplicates? title authors doi published_in year Coseismic extension recorded within the damage zone of the Harold Leah, Michele Fondriest, Alessio Lucca, Duplicates 10.31223/osf.io/5y3pn 2018 BUT ! Vado di Ferruccio Thrust Fault, Central Apennines, Italy Fabrizio Storti, Fabrizio Balsamo, Giulio Di Toro Coseismic extension recorded within the damage zone of the Harold Leah, Michele Fondriest, Alessio Lucca, 10.1016/j.jsg.2018.06.015 Journal of Structural Geology 2018 Vado di Ferruccio Thrust Fault, Central Apennines, Italy Fabrizio Storti, Fabrizio Balsamo, Giulio Di Toro Determination of cefpirome concentrations in lung extracellular Croneberger, A.S., Kietzmann, M., Ehinger, Duplicates 10.1111/j.1365-2885.2009.01090.x Journal of Veterinary Pharmacology and Therapeutics 2009 BUT ! fluid of pigs by microdialysis and ….. A.M., Allan, M., Nuernberger, M.C. Not Oral communications 10.1111/j.1365-2885.2009.01090.x Journal of Veterinary Pharmacology and Therapeutics 2009 Médecine & Droit; The judge, the physician and the prisoner. A Duplicates Chassagne A, Godard-Marceau A 2019 critical view of the “suspension de peine” for medical reason Le juge, le médecin et le détenu. Regard critique sur la Chassagne A, Godard-Marceau A 10.1016/j.meddro.2019.01.001 Medecine et Droit 2019 suspension de peine pour raison médicale Duplicates Hearing loss: Diagnosis and Management John M Lasak, Patrick Allen, Douglas Lewis Primary Care Clinics in Office Practice 2014 Not John M Lasak, Patrick Allen, Tim McVay, Hearing loss: diagnosis and management. Primary care 2014 Douglas Lewis On the number and structure of sum-free sets in a segment of Duplicates K. G. Omelyanov,A. A. Sapozhenko Discrete Mathematics and Applications 2002 positive integers On the number and structure of sum-free sets in a segment of K. G. Omelyanov,A. A. Sapozhenko Discrete Mathematics and Applications 2003 positive integers Vertex-disjoint cycles containing specified edges in a bipartite Guantao Chen, Hikoe Enomoto, Ken Ichi Duplicates Dicscrete Mathematics(Elsevier) 2001 graph Kawarabayashi, Katsuhiro Ota… Not Vertex-disjoint cycles containing specified vertices in a bipartite Guantao Chen, Hikoe Enomoto, Ken Ichi 2004 graph Kawarabayashi, Katsuhiro Ota… 9

10.Improving the classifier: What have we done? Bootstrap with previous training set Engineer relevant features Find document Tune & Train pairs at the Benchmark model decision boundary Heurristic + Manual Classification Deploy 10

11.Scalability issues: Reduce problem cardinality Record exclusion Data normalization Exact duplicates detection and Identifier based duplicates detection Tuneable blocking step MinHash-LSH and sub blocking if needed Divide classifier tasks by 2 by removing equal pairs (A,B) = (B,A) 11

12. Performance Old New F1 Score New manually Old data set annotated data Comute time (1M pairs) set (9.2k pairs) Old 0.98 0.60 >24h Dedup new 0.98 0.96 ~13h Dedup Verdict Same Much better Much better If 3% error rate, over 2B => 60M miss classified 12

13.Lessons learned • Monitor your systems • Data matters ! − Know your data! − “More data” vs “Good data” • Engineering − Keep it as simple as possible If it works with simpler model don’t use more complex ones e.g. Random Forest vs SVM with RBF kernel vs Neural Networks − Extensive testing Especially if production code will use different language / libraries / library versions − With big data, hash collision is real • As a data scientist, work in tight collaboration engineers implementing production grade code Make sure things are feasible within their tech stack 13

14. Come join us in solving problems 7,500 >1,000 Empoyees Thank you technologists https://www.elsevier.com/about/careers/technology-careers 15

15.DON’T FORGET TO RATE AND REVIEW THE SESSIONS SEARCH SPARK + AI SUMMIT