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NASIG 2012 - Discovering the World's Research (ITHAKA portion)


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NASIG 2012 - Discovering the World's Research (ITHAKA portion)

  1. 1. Discovering the World’s Research Ron Snyder Director of Advanced Technology, ITHAKA/JSTOR NASIG Annual Conference - 2012 June 9, 2012
  2. 2. Who we are ITHAKA is a not-for-profit organization that helps the academic community use digital technologies to preserve the scholarly record and to advance research and teaching in sustainable ways. We pursue this mission by providing innovative services that aid in the adoption of these technologies and that create lasting impact. JSTOR is a research platform that enables discovery, access, and preservation of scholarly content.
  3. 3. JSTOR Factoids • Started in 1997 • Journals online: 1,604 • Articles online: 7.5 million • Disciplines covered: 60 • Participating institutions: 7,800 • Countries with participating institutions: 167
  4. 4. JSTOR site activity User Sessions (visits) » New Sessions (per hour):  70k peak, 38k average » Simultaneous Sessions:  44k peak, 21k average Page Views » 3.5M per day, 6.7M peak Content Accesses » 430k per day, 850K peak Searches » 456k per day, 1.13M peak
  5. 5. ITHAKA/JSTOR Discovery Initiatives • Overhaul of JSTOR Search Infrastructure • Coming Soon (Summer 2012), watch for it… • Analytics and data warehouse • Ingesting, organizing, and analyzing billions of usage events since JSTOR inception • Improved external discoverability • Various SEO, Google/GS, MS-Academic projects • Local Discovery Integration (LDI) Pilot • Machine-based document classification
  6. 6. Local Discovery Integration Pilot JSTOR and Summon
  7. 7. Problem Statement: » Research has shown time and again that both students and faculty are beginning their research at places other than the library OPAC, most notably Google/Google Scholar and discipline-specific electronic databases, and that the trend is continuing Starting point for research, identified by faculty in 2003, 2006, and 2009 (2009 Faculty Study, ITHAKA) 100% 90% 2003 80% 2006 70% 2009 60% 50% 40% 30% 20% 10% 0% The library building online librarygeneral-purpose specific engine Your A catalog A search electronic research resource
  8. 8. Where is discovery happening? Where JSTOR ‘sessions’ originated | Jan 2011 – Dec 2011
  9. 9. Problem Statement: » As web-scale discovery services are being purchased and implemented by institutions, the value of those implementations are somewhat limited because they are (for the most part) only addressing that limited population of researchers who begin at a library-designated starting point (e.g. OPAC) JSTOR usage | Australia | 2010 Nov. JSTOR Google/Google Scholar Known Linking Partner Library 16% 6% 9% 76% 2%
  10. 10. Research Behavior: Students What is the easiest place to start research according to students?Library Databases Google 0 10 20 30 40 50 60 70 Source: ProQuest survey of student research habits, 2007
  11. 11. Research Behavior: Faculty Starting Point for Research, identified by faculty in 2003, 2006, and 2009 100% 90% 2003 80% 2006 70% 2009 60% 50% 40% 30% 20% 10% 0% The library building online librarygeneral-purpose specificengine Your A catalog A search electronic research resource Source: ITHAKA 2009 Faculty Survey, 2010
  12. 12. Concept: » If we can more effectively reach the users at the place(s) where they normally begin their research, then we can begin to more effectively build their awareness of the resources that the institution has licensed/purchased for their purposes » The local discovery integration (LDI) pilot study will attempt to measure changes in the student/faculty research experience by „embedding‟ the institution‟s selected web-scale discovery service in strategically-selected places in the JSTOR interface where – we believe – the user would naturally want to „cast a wider net‟ for discovery 2010 JSTOR Usage Highlights Total Significant Accesses 594,888,001 Articles Downloaded 74,901,344 Articles Viewed 112,751,906 Searches Performed 168,720,887 Inbound Links from Licensed Partners 13,013,904 Inbound Links from Google/Scholar 157,903,053
  13. 13. How it works Links Out • Search Results  Advanced Search Page  Search Results View • 3rd Page “Lightbox” pop-up • Article View - Incoming from Google • Article View - All other non-Google • Zero Results PageWe placed links at various places along the research workflow inJSTOR to allow students and researchers to “Cast a wider net”
  14. 14. Search results page » JSTOR may not be the most appropriate starting place in every instance, but it is a trusted and familiar interface. This will allow the user to „flowback‟ to another starting place (e.g. the library) • Uses the familiar university logo to grab attention • Inserts search terms into link text to notify user of customized behavior • Positioned proximate to search results; relevant during the search result evaluation phase
  15. 15. Empty results page » In this instance, the user has found nothing and the most typical web response is to hit the „Back‟ button. If we allow the user – at this point – to execute a search in the local discovery interface, we might improve the user experience • One of the key places where a user is likely to want to try a different, broader search • Larger placement takes advantage of available real estate and cognitive space • Users typically do not spend time on this page so it is important to increase notice-ability and self- explanation
  16. 16. Article page after Google search » In 2010, over 32M Google/Google Scholar searches brought users directly to an article page. They may or may not have found what they really wanted, so we‟d like to give them an alternative discovery choice • Visible when coming from a Google or Google Scholar search • Captures basic search terms from the search • Provides an opportunity to convert a user from a Google/Google Scholar user to a Summon user
  17. 17. Article page after JSTOR search » In 2010, almost 113M articles were viewed in JSTOR. Again, they may not have found what they really wanted, so we‟d like to give them an alternative discovery choice • Visible when coming from a JSTOR search • Raises visibility of the feature by exposing it to a large number of users • Inserts search terms into link text to notify user of customized behavior
  18. 18. Results View: All PagesLink out from thebottom of all pages ofthe search results view.This will allow moreopportunities to link outfor students/ researcherscombing through largesets of results.
  19. 19. Results View: 3rd PagePop-up on the third page of search resultsPrompts the student/ researcher to indicate whether they wish to link out through the LDI. Thiswill enable us to measure whether students wish to “cast a wider net” or not. In the other linkscenarios we don’t have a baseline of how many students do not notice the link vs. choose notto use it
  20. 20. Link out to Discovery Platform
  21. 21. Results Overview » Highest usage occurred in Zero Results scenarioData shown is for all institutions participating in Summon LDIDate range: July 2011 – February 2012
  22. 22. Machine-Based Article Classifier Assigning Articles to Disciplines
  23. 23. The Problem JSTOR Corpus • 60 disciplines • 1,600 journals • Nearly 8 million articles • Disciplines are associated at the Journal level • All articles in a Journal inherit the Journal assigned disciplines • Using this approach many articles have incomplete and/or incorrect discipline tagging hindering discovery • How to assign disciplines to articles?
  24. 24. Topic Models • Human classification and tagging is not feasible • A machine-based classification process is desired • Topic models are a way of finding structure in a set of documents • They allow is to find “latent” themes • A topic model is not a topic map • Some topic modeling approaches include • Latent Semantic Analysis (LSI/LSA) (Deerwester 1990) • Probabilistic LSA (Hoffmann 1999) • Latent Dirichlet Allocation (LDA) (Blei 2003)
  25. 25. Topic Modeling – our approach LDA – Latent Dirichlet Allocation • A generative probabilistic model for analyzing collections of documents • A Bayesian model where each document is modeled as a mixture of topics (disciplines) • Models semantic relationships between documents based on word co-occurrences
  26. 26. The Process • We select the most representative documents from each JSTOR discipline to build a topic model (from the vocabulary of the document sample) • This sampling and vocabulary modeling is the most important part of the process! • We’re still experimenting with this, but find the citation network provides a good means for identifying core documents in a discipline • Also considering whether usage data might be leveraged here • Each document in the corpus is then analyzed and compared to the topic model to determine how well it matches each topic • A probability distribution is generated providing discipline weights • The top weighted discipline(s) are associated with each article
  27. 27. Application • On-site discovery • Will be a key element of our overhauled search infrastructure, tentatively scheduled for beta release mid-summer • Use in article-level discipline/subject/topic mappings for better integration with aggregated indexes • Will support a richer data feed for Summon, for instance