Presented at the 5th International Conference on Qualitative and Quantitative Methods in Libraries (QQML), "La Sapienza" University, Rome, Italy, June 6, 2013.

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

2. Faculty research interests
Publication records also are very useful, if parsed w

3. Advanced faculty profiles: The
Opus project at UCLA
(envisioned)

4. Faculty CV analysis in
RapidMiner

5. TF-IDF vectors of faculty
interests
study,0.833333333
history,0.48984375
work,0.259709821
center,0.142113095
culture,0.218452381
artwork,0.322321429
southeast,0.012946429
architecture,0.12797619
Asian,0.066145833
bronze,0.0375
research,0.196875
translation,0.055133929
south,0.071428571
design,0.071130952
image,0.078683036
museum,0.145982143
place,0.071316964
ancient,0.03813244
Chinese,0.036830357
national,0.094791667
light,0.028869048
landscape,0.033854167
project,0.065848214
field,0.058928571
state,0.052380952
material,0.050892857
review,0.069568452
east,0.060639881
Indian,0.023214286
make,0.080729167
life,0.034375
discipline,0.113095238
change,0.050558036
Asia,0.02641369
society,0.047209821
form,0.04077381
cultural,0.070833333
salt,0.010044643
institute,0.07421875
house,0.009821429
china,0.023809524
survey,0.085044643
period,0.01875
report,0.083035714
space,0.032291667
serve,0.040848214
learn,0.081026786
world,0.044270833
gallery,0.025446429
analyze,0.084077381
build,0.016666667

6. Monograph records from
WorldCat
$worldCatQuery =
“(srw.lc+all+N1*+or+srw.lc+all+N2*+or+srw.lc+a
ll+N3*+or+srw.lc+all+N4*+or+srw.lc+all+N5*+or
+srw.lc+all+N6*+or+srw.lc+all+N7*+or+srw.lc+a
ll+N8*+or+srw.lc+all+N9*+or+srw.lc+all+NB*+or
+srw.lc+all+NC*+or+srw.lc+all+ND*+or+srw.lc+
all+NE*+or+srw.lc+all+NK*+or+srw.lc+all+NX*+
or+srw.lc+all+TR*)+and+srw.yr>2005+and+srw.
yr<2014+and+srw.mt+any+bks+and+(srw.la+all
+eng+or+srw.la+all+fre+or+srw.la+all+ger+or+s
rw.la+all+ita+or+srw.la+all+spa+or+srw.la+all+d
ut+or+srw.la+all+por)+not+srw.mt+any+juvenile
+not+srw.mt+all+ebk+not+srw.mt+all+elc”;
Process all ~160,000 results…

7. Monograph records from
WorldCat

8. Cosine similarity between faculty
profiles and book records
French Impressionism
Early
Qing
painting

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)

14. Circulation of actual selections
vs. simulated algorithmic
selections

15. Circulation of actual selections
vs. simulated algorithmic
selections

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