A SINGLE SEARCH BOX IS NOT ENOUGH Transforming Discovery with the Summon® service, 360 Link and ProQuest Flow Nicolas Espeche ProQuest Workflow Solutions

Meetings Users’ Expectations For the challenge that libraries face is that user expectations are always evolving and libraries must keep up. .. The reality is that user expectations are always evolving and libraries are always going to be challenged to keep up…the danger for libraries is that the further they are disconnected from meeting user expectations the more they will be disintermediated from the research process. (and risk irrelevance in the minds of researchers)

Understanding today’s users Discovery starts with meeting users’ expectations. … starts with understanding who today’s users are … The key to this is not only understanding who our users are but also understanding how they use the library and discovery service. Self sufficient and do NOT ask librarians for help Expect everything to be online and immediately accessible Use multiple devices and are increasingly mobile Want to share and collaborate

Presented by Alison Head. ACRL conference, April 11, 2013 That brings me back to the work of Project Information Literacy – Alison Head presented these research findings at the most recent ACRL conference as top take-aways from surveys of 11,000 students at 57 colleges and universities. What they found is: large numbers of students find it difficult to get started with the research process – especially defining a topic. 80% of students never ask a librarian for help Most of your users are using Wikipedia for background information (and the only problem with that is that many of these students don’t come back to the library) Presented by Alison Head. ACRL conference, April 11, 2013

Understanding user search behaviors High frequency of misspellings Natural language queries are common Users often edit search terms rather than seek new strategies 85% of users never go past first page of results 45% of Searches 3 words or less Lets put this chart into perspective using a logarithmic scale so you can see. Majority of searches are 1, 2, and 3 word queries. Number of terms per query Summon query analysis – Erin Dorris Cassidy , Glenda Jones , Lynn McMain , Lisa Shen & Scott Vieira (2014) Student Searching with EBSCO Discovery: A Usability Study, Journal of Electronic Resources Librarianship, 26:1, 17-35, DOI: 10.1080/1941126X.2014.877331

System architecture matters. The cornerstone of Summon is the unified index. Pre-harvest For normalizing metadata Nobody else does what we are doing – one of kind! Built for: For speed For consistent search For superior relevance ranking unified result set / no-compromise user experience / built-for-purpose Google doesn’t make you run another search to find the rest / Simple. Easy. Fast. System architecture matters.

The Summon model is different… Architecture of the system matters! Unified Results Set Unified Index The cornerstone of Summon is the unified index. Pre-harvest For normalizing metadata Nobody else does what we are doing – one of kind! Built for: For speed For consistent search For superior relevance ranking unified result set / no-compromise user experience / built-for-purpose Google doesn’t make you run another search to find the rest / Simple. Easy. Fast. Over 1.4 billion items from 10,000+ content providers and more than 90+ content types Integrated rights management Indexed Enhanced Direct Linking Architecture impacts speed, relevancy, ease of navigation, currency, customization options and more Having the critical core of the library’s collection represented in a single, unified index is the key to making it all work speed, ease of use, relevancy, customizability, research integrity, stability/reliability, and more

Match and Merge Technology Summon Record Full Text from Elsevier, Sage, Wiley Abstracts, subject terms, etc. Gale, ProQuest DOI CrossRef Peer-reviewed status Ulrich’s Citation counts, subject terms, etc. from Web of Science Scopus The unified index architecture allows us to richest possible metadata is available for discovery. Normalization, Correction and Deduplication of metadata across all sources. Not just from a single content vendor or platform. Not reliant on thin metadata from citation sources like CrossRef. All of this work happens prior to indexing.

Unprecedented Coverage 10,000+ publishers represented 150,000+ journals at the article-level, most full-text searchable Over 1.4 Billion Summon records - 90 different content types Content Number of records Newspaper Articles 530 million + Journal Articles 220 million + Books & eBooks 200 million + Theses and Dissertations 22 million + Conference Proceedings 21 million + Patents 9.8 million + Research Guides 29,000+ Library Catalog records 235 million + Open Access records 28.5 million + Institutional Repository records 16.5 million + Government Documents 23.9 million + Summon has clearly passed the critical mass of content held by libraries. All of the content is searched the same way – through our massive index of indexed metatdata and full-text. All of the results from all of the providers are given the same weight. So, whether a good result comes from ProQuest, Wiley, Nature, or the Library catalog, they all have an equal chance of appearing on the result list depending upon relevancy ranking.

Relevancy across a unified index True relevance ranking across all content Relevance Ranked Results List Dynamic Ranking Static User Query Recommendations Dynamic Ranking Term Frequency (inverse) Document Frequency Field Weighting Term Stemming Stop Word Processing Static Ranking Content Type Recency/Publication Date Scholarly/Peer Review Boost local collections Citation counts While this model over-simplifies the complexity associated with applying relevance ranking algorithms across a body content as large as the Summon index, Summon offers advanced relevancy tuning that is unmatched by any other discovery service. First it is important to understand how Summon works. The fact that we do return results from a single, unified index across the full breadth of the library’s collection, means that we actually do have all of the results available to apply a relevance ranking algorithm to. Of course Summon uses Dynamic Relevance Ranking – the types of things you’d expect such as term frequency, word proximity, field weighting, etc.; but we also use Static Ranking. Just as Google uses “page rank” to boost web pages that are hit more often, Summon uses Static Rank to boost items by different factors. Local content and books might get boosted, as would scholarly or more recent articles, while a less scholarly item might get downgraded in the relevancy. All results come back in a single, unified result set. But the user is still given unparalleled options for using facets and limiters that apply to all of the content equally allowing the user to zoom in on the most relevant results. In addition Summon can even provide context sensitive recommendations to other content sources, research guides, etc. that may aide the user. http://media2.proquest.com/documents/Summon-RelevanceRanking-Datasheet.pdf

Live Demo 9/13/2019