Let Data Guide Us Into The Next Discovery Experience: A Collaborative Approach to Optimizing Primo VE Search Configurations

Li, Can, Cribbs, Heather, Ward, Christian and Gardner, Gabriel Let Data Guide Us Into The Next Discovery Experience: A Collaborative Approach to Optimizing Primo VE Search Configurations., 2024 . In 2024 eCAUG, Long Beach, CA, October 24-25, 2024. (Unpublished) [Presentation]

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English abstract

Our presentation explores a collaborative effort among CSU campuses, led by members of the Data Issues Task Force, to optimize Primo VE search configurations with the goal of enhancing search results and user satisfaction across the system. Each campus employs unique settings for search and relevancy ranking, resulting in varied user experiences. Through a comprehensive analysis of these configurations using a shared spreadsheet, we identified patterns and inconsistencies that impact search outcomes. Our aim is to determine the optimal configurations and establish baseline analytics, ensuring that end users can efficiently complete their top tasks, especially with the new discovery UI set to launch in 2025. A key component of our analysis is the use of the Primo Search API, which enabled us to automate the collection and comparison of data on search queries, results, and relevancy rankings across campuses. The API provided valuable insights into common search challenges and effective configurations, supporting our data-driven approach to optimizing search functionality. As we refine these settings in preparation for the upcoming Next Discovery Experience, we invite further collaboration from other CSU campuses. By broadening our efforts under the guidance of the Data Issues Task Force, we can develop a unified approach that ensures consistent, high-quality user experiences across the CSU system.

Item type:	Presentation
Keywords:	Primo, relevance, ranking
Subjects:	J. Technical services in libraries, archives, museum. > JZ. None of these, but in this section. L. Information technology and library technology > LR. OPAC systems. L. Information technology and library technology > LS. Search engines.
Depositing user:	Gabriel Gardner
Date deposited:	17 Feb 2025 16:58
Last modified:	17 Feb 2025 16:58
URI:	http://hdl.handle.net/10760/46225

References

Corbett, P. (2010). What about the “Google Effect”? Improving the Library Research Habits of First-Year Composition Students. Teaching English in the Two-Year College, 37(3), 265–277. https://doi.org/10.58680/tetyc201010232

Brett, K., Lierman, A., & Turner, C. (2016). Lessons Learned: A Primo Usability Study. Information Technology and Libraries, 35(1), 7–25. https://doi.org/10.6017/ital.v35i1.8965

Dan, O., & Davison, B. D. (2016). Measuring and Predicting Search Engine Users’ Satisfaction. ACM Comput. Surv., 49(1), 18:1-18:35. https://doi.org/10.1145/2893486

Bothra, D. & Gautam, A. (2024, June 28). The Whys and Hows of Measuring Search Relevance. https://www.searchunify.com/blog/the-whys-and-hows-of-measuring-search-relevance/

Dixon, C. (2024, October 16). Case comments are added to the case: 07719726: Blended search profile boosting [Personal communication].

Galbreath, B. L., Merrill, A., & Johnson, C. M. (2021). Framework for Measuring Relevancy in Discovery Environments. Information Technology and Libraries, 40(2), 1–17. https://doi.org/10.6017/ital.v40i2.12835

Hughes, E. (2018). Web Search Engines—Not Yet a Reliable Replacement for Bibliographic Databases. Evidence Based Library and Information Practice, 13(3), 85–87. https://doi.org/10.18438/eblip29378

Li, C. (August 2024). Configure Primo VE Search Result Ranking with Clarity: A Workable Method Using API. Internet Reference Services Quarterly, 1–18. https://doi.org/10.1080/10875301.2024.2394227

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