Presented at the 5th International Conference on Qualitative and Quantitative Methods in Libraries (QQML), "La Sapienza" University, Rome, Italy, June 6, 2013.
aiSelections: Computational Techniques for Matching Faculty Research Profiles to Library Acquisitions
1. aiSelections: Computational
Techniques for Matching
Faculty Research Profiles to
Library Acquisitions
Peter M. Broadwell – CLIR Postdoctoral Fellow, UCLA
Library
Timothy R. Tangherlini – Professor, Scandinavian Section
and
Department of Asian Languages,
UCLA
9. Cosine similarity between faculty
profiles and book records
French Impressionism
Early
Qing
painting
UCLA faculty
10. Cosine similarity between faculty
profiles and book records
French Impressionism
Early
Qing
painting
UCLA faculty
Book A
Book B
11. Cosine similarity between faculty
profiles and book records
French Impressionism
Early
Qing
painting
UCLA faculty
Book A
Book B
12. Cosine similarity between faculty
profiles and book records
French Impressionism
Early
Qing
painting
UCLA faculty
Book A
Book B
13. Evaluation data sets
Actual selections, Jan 2007 – Feb 2013
◦ 10,471 books in targeted subject areas, published
after 2005 (subset of WorldCat data set, described
below)
◦ 3,573 firm orders, 6,989 approval plan orders
Circulation records, Jan 2008 – Feb 2013
◦ 4,118 new, unique titles acquired after Jan 2007
circulated between Jan 2008 and Feb 2013
◦ This is 39.3% of acquisitions since 2007
◦ Firm orders were 10% more likely to circulate
◦ 606 books published since 2006 were borrowed
via interlibrary loan, many at no cost (intra-UC)
All potential selections, published 2006-
2012
◦ 130,042 unique titles (duplicates resolved)
16. Faculty profile matching:
Applications and considerations
Append a “faculty match” score to
vendor approval list entries
◦ Helps to target selections for the short
and medium term
◦ Not as useful for long-term, large-scale
collection development
Refine subscriptions to online
periodicals and other resources
◦ Requires that online subscriptions can be
done a la carte, rather than via bulk
packages
17. Faculty profile matching:
Future directions
Enhance faculty profiles
◦ Promising, due to growth in publication
bibliometrics, faculty network analysis tools
like Vivo and Profiles
Enhance resource profiles by obtaining
more data
◦ For pre-publication monographs: unlikely
◦ Might be possible with online publications
Incorporate graduate student,
undergraduate research interests
Combine circulation-based selection
recommendations with faculty interest
data
18. aiSelections: Computational
Techniques for Matching
Faculty Research Profiles to
Library Acquisitions
Peter M. Broadwell – CLIR Postdoctoral Fellow, UCLA
Library
Timothy R. Tangherlini – Professor, Scandinavian Section
and
Department of Asian Languages,
UCLA