“ Can You Dig It?” A Systems Perspective Dave Pattern Library Systems Manager University of Huddersfield [email_address]

“There comes a time in every rightly constructed boy’s life that he has a raging desire to go somewhere and dig for hidden treasure.” Mark Twain, “The Adventures of Tom Sawyer”

“There comes a time in every rightly constructed boy’s life that he has a raging desire to go somewhere and dig for hidden treasure.”

Mark Twain, “The Adventures of Tom Sawyer”

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.”

“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.”

Libraries…? “ Libraries 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.”

“ Libraries 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.”

Libraries…? “Libraries could gain valuable insights into user behaviour by data mining borrowing and uncovering usage trends. Further insights could be gained by analysing borrowing histories, facilitated by the use of library cards.” …so, why aren’t we all busy doing it?

“Libraries could gain valuable insights into user behaviour by data mining borrowing and uncovering usage trends. Further insights could be gained by analysing borrowing histories, facilitated by the use of library cards.” …so, why aren’t we all busy doing it?

Borrowing at Huddersfield 13 years of circulation data details of over 3 million transactions a goldmine of information… …unfortunately, nobody was doing anything with the data!!!

13 years of circulation data

details of over 3 million transactions

a goldmine of information…

…unfortunately, nobody was doing anything with the data!!!

Borrowing Suggestions

Borrowing Suggestions suggestions are generated by data mining the historical transaction data ranking algorithm: number of people who borrowed both books A and B, divided by the total number of loans for book B …this helps avoid the “Harry Potter Effect”

suggestions are generated by data mining the historical transaction data

ranking algorithm:

number of people who borrowed both books A and B, divided by the total number of loans for book B …this helps avoid the “Harry Potter Effect”

Personalised Suggestions

Personalised Suggestions generated by aggregating the recommendations for items most recently borrowed …with a sprinkling of randomness!

generated by aggregating the recommendations for items most recently borrowed …with a sprinkling of randomness!

Lending Paths (beta)

Lending Paths (beta) if we’re recommending book B, is it normally borrowed before or after book A? can we use that information to make predictions about future borrowing and give more timely recommendations?

if we’re recommending book B, is it normally borrowed before or after book A?

can we use that information to make predictions about future borrowing and give more timely recommendations?

Usage Statistics for Staff

Usage Statistics for Staff

Usage Statistics for Staff

Usage Statistics for Staff

OPAC Keyword Searches all keyword searches are logged the data can be mined to provide: prompts/suggestions for searches trends in keyword usage common keywords that fail to produce results (e.g. “renew”)

all keyword searches are logged

the data can be mined to provide:

prompts/suggestions for searches

trends in keyword usage

common keywords that fail to produce results (e.g. “renew”)

Keyword Suggestions

Keyword Cloud

Common Zero Result Keywords “newspapermen” “estimation” “superficial” “ligament” …also “asbo”, “chav”, “disneyfication”, “newszak” and “sudoku”! …is this useful information for collection development?!

“newspapermen”

“estimation”

“superficial”

“ligament”

…also “asbo”, “chav”, “disneyfication”, “newszak” and “sudoku”!

…is this useful information for collection development?!

“Can You Dig It?” we’ve found gold in our own data and our services are richer for it… “Prospector” http://www.flickr.com/photos/tooliver/497511638/

we’ve found gold in our own data and our services are richer for it…

“Can You Dig It?” there’s gold in your data… “Old Gold Mine” http://www.flickr.com/photos/splatt/1058989589/

there’s gold in your data…

“Can You Dig It?” what if we collaborate? …I’ll show you mine (my mine?), if you’ll show me yours! Norwegian mining plant http://www.flickr.com/photos/bzzzt/2458300944/

what if we collaborate? …I’ll show you mine (my mine?), if you’ll show me yours!

Huddersfield’s Usage Data aggregated usage & recommendation data Open Data Commons licence http:// library.hud.ac.uk/usagedata /

aggregated usage & recommendation data

Open Data Commons licence

http:// library.hud.ac.uk/usagedata /

Huddersfield’s Usage Data usage data for over 80,000 titles and 2,000,000 transactions, broken down by: year academic school academic course (with relevant UCAS codes) recommendation data for over 37,000 titles

usage data for over 80,000 titles and 2,000,000 transactions, broken down by:

year

academic school

academic course (with relevant UCAS codes)

recommendation data for over 37,000 titles

“Let’s Work Together” I’ve shown you mine …now what’s stopping you from showing me yours? “peek-a-boo” http://www.flickr.com/photos/96221617@N00/51603550/

I’ve shown you mine …now what’s stopping you from showing me yours?