Download Slide 1

First year History students
searching for Napoleon
T I L E
Also borrowed …
They downloaded …
They rated this resource as …
They also recommended ...
D
David Kay
Sero Consulting
[email protected]
Sitting on a goldmine
- the value of
attention and activity data
Slides & ideas stolen from
• David Jennings, author ‘Net blogs & rock ’n’ roll’
• Dave Pattern, Huddersfield
• Mark van Harmelen, Hedtek
• Paul Walk, UKOLN
• Caroline Williams, Mimas
2
Supermarkets
“Supermarkets gain valuable insights into user behaviour
by data mining purchases and uncovering usage
trends.
Further insights are gained by analysing purchasing
histories, facilitated by the use of store loyalty cards.”
Dave Pattern, University of Huddersfield – December 2008
Libraries?
“Libraries could gain valuable insights into user
behaviour by data mining borrowing and uncovering
usage trends.
Further insights are gained by analysing borrowing
histories, facilitated by the use of library cards.”
Dave Pattern, University of Huddersfield – December 2008
Distributed …
Content & finding aids
anywhere & any type
Concentration of …
Context data
Catalysing contribution
What’s
recommended
in the VLE?
Did anyone
highly rate this
textbook?
Across …
An Institution
A Consortium
A national system
Global communities
What’s did last
year’s students
download most?
What’s the
economics take
on this topic?
What do
undergraduates
elsewhere read?
Resources other people are using
(CERLIM research for MOSAIC)
• 90% of students said
they would like to be
able to find out what
other people are
using
Finding out what resources other people are
using
50
40
30
20
10
0
45
42
32
People taking the same People taking a similar
course as you at
course as you:
another university:
Lecturers teaching on
your course:
Finding out what resources other people are
using
30
25
20
15
10
5
0
27
21
14
Lecturers teaching a
similar course at
another university:
People taking the same People taking a similar
course as you in a
course at a different
different year of study: university and a different
year of study:
California
State
University
2008
What can be done in the library?
Dave Pattern - Huddersfield
• Raw popularity enables recommendation
• Lending paths enable sequential recommendation What people borrow next
• Dewey matches enable recommendation from new
book lists at course level based on Dewey-based
patterns of borrowing
• Search histories yield Search clouds, Search
combinations that work (law try criminal law, civil law),
Search terms that ‘discover’ books
suggestions based on circ data
“people who borrowed this…”
getting personal!
suggestions for what to borrow next
the impact on borrowing
average number of books borrowed
17
16
15
14
13
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
average number of books borrowed per active borrower per calendar year (2009 predicted)
usage data for staff
loans per academic school
Motivation
Attention
Contribution
Work: PLF – 18 May, 2008 - 6 versions
Activity (Downloads Served)
& Contributions
V1 – 1192 DL – 30 comments
V2 – 1389 DL
V3 – 4517 DL – 5 reviews
V4 – 1338 DL
V5 – 285 DL – 1 review
V6 – 2702 DL – 1 review
ALL – 11423 DL – 37 contributions 15
Aggregate niche to deliver network ?
Motivation
Attention
Motivation
Attention
Motivation
Attention
Contribution
Contribution
Contribution
Motivation
Attention
Motivation
Attention
Motivation
Attention
Contribution
Contribution
Contribution
Is it desirable and feasible
to combine the outputs of
niche communities
into a large network?
‘… and the more we track, the better
we can adapt our service without your intervention.’
The
Institution
Teaching
&
Learning
The
Library
Research
The
Network
Who do you serve?
Niche / specialist networks
• Automated recommendation systems may be of
interest at undergraduate level
• But, in academia, beyond undergraduate it’s long tail
all the way!
• Small networks based on people actually knowing
each other
• Networks provide the opportunity for other people,
known to me, to intervene on my behalf, thus reducing
‘filter-failure’
19
Some Questions
• Does activity data need to be aggregated above the
institutional level to achieve web scale and deliver network
effect?
• Amazon tells you that ‘people who did this also did that’. Can
academic libraries offer something more significant (‘people
LIKE YOU who did this also did that’) because they know the
user’s context (typically their course and institution)?
• Precision in such as metadata and even citation is subject to
personal judgements and motivations. Are these less reliable
than the pointers derived from a mass of contextualised
activity data?
• As proxies for real activity, are lists – formal reading lists,
informal student lists – a form of attention data which can be
highly weighted?
15-07-2010 Workshop
Some Next Steps
• Joint work on standards and specs with publishers
and systems vendors
• Solutions for the key requirement of context
• Reliable subject (course / module) identifiers, which may or may not be
UCAS or JACS codes
• Look beyond just books and just use data