Today I’m at Mash Oop North aka #mashlib09 – and kicking off with a presentation from Dave Pattern – some very brief notes:

Making Library Data Work Harder

Dave Pattern – www.slideshare.net/daveyp/

Keyword suggestions – about 25% of keyword searches on Huddersfield OPAC give zero results.
Look at what people are typing in the keyword search – Huddersfield found ‘renew’ was a common search term – so can pop up a information box with information about renewing your books.

By looking at common keyword combinations can help people refine their searches

Borrowing suggestions – people who borrowed this item, also borrowed …
Tesco’s collect and exploit this data. Do libraries sometimes assume we know what is best for our users – but we perhaps need to look at data to prove or disprove our assumptions

Because borrowing driven by reading lists, perhaps helps suggestions stay on-topic

Course specific ‘new books’ list – based on what people on specific courses borrow
Able to do amazon-y type personalised suggestions

Borrowing profile for Huddersfield – average number of books borrowed shows v high peak in October, lull during the summer – now can see the use of the suggestions following this with a peak in November.

Seems to be a correlation between introduction of suggestions/recommendations with increase in borrowing – how could this be investigated further?

Started collecting e-journal data via SFX – starting to do journal recommendations based on usage.

Suggested scenario – can start seeding new students experience – 1st time student accesses website can use ‘average’ behaviour of students on same course – so highly personalised. Also, if information delivered via widgets could drag and drop to other environments.

JISC Mosaic project, looking at usage data (at National level I think?)

So – some ideas of stuff that you might do with usage data:

#1 Basic library account info:
Just your bog standard library optionss
– view items on loan. hold requests etc
– renew items
Confgure alerting options
– SMS, Facebook, Google Teleppathy
Convert Karma
– rewards for sharing information/contributing to pool of data – perhaps swap karma points for free services/waiving fines etc.

#2 Discovery service
Single box for search

#3 Book recommendations
Students like book covers
Primarily a ‘we think you might be interested in’ service
Uses database of circulation transactions, augmented with Mosaic data
time relevant to the modules student is taking
Asapts to choices student makes over time

#4 New books
Data-mining of books borrowed by student on a course
Provide new books lists based on this information (already doing this at Huddersfield I think)

#5 Relevant Journals

#6 Relevant articles
– Whenever student interacts with library services e.g. keywords etc. – refines their profile