Agreed - it's a great idea in theory, but I'm not sure how well it would work
in actual practice. Even in a single library, genre subject headings are
usually pretty inconsistent in the MARC records because of copy cataloging, and
that usually gets even more inconsistent in a consortium of libraries. Perhaps
it could be partially weighted on genre subject headings, but not overly
reliant on them? It might be worth considering the fixed field values for
fiction vs. non-fiction and for age groups, too.
I love the idea of providing recommendations based on other people that have
similar taste ("other people that liked this book also liked these books...")
but if the data is tied to actual patrons (and I'm not sure how it couldn't be)
then quite a few library systems would face legal privacy issues and wouldn't
be able to use it. We're currently using a commercial service to pull in
reading recommendations because the recommendations can't be tied back to any
of our patrons.
Terran McCanna
PINES Program Manager
Georgia Public Library Service
1800 Century Place, Suite 150
Atlanta, GA 30345
404-235-7138
[email protected]
----- Original Message -----
From: "Rogan Hamby" <[email protected]>
To: "Evergreen Discussion Group" <[email protected]>
Sent: Thursday, September 25, 2014 2:02:58 PM
Subject: Re: [OPEN-ILS-GENERAL] Awesome Box Integration
I can see some challenges to tracking genre and I'd be hesitant to put too much
value on it. There are ways to catalog it but in my experience actually relying
on it being in records (much less being consistent) is very unreliable in
organizations that do a lot of copy cataloging / don't have centralized and
controlled cataloging and there quite a few in that boat.
That concern aside, I've always thought this would be a fun and potentially
valuable thing to add.
On Thu, Sep 25, 2014 at 1:44 PM, Vanya Jauhal < [email protected] > wrote:
Hello everyone
I'm Vanya, from India. I'm a candidate for OPW Round9 internship with
evergreen.
While discussing the idea of Awesome Box integration with Evergreen, Kathy and
I discussed the possibility of making the Evergreen support for Awesome Box
more interpretive using Artificial Intelligence.
What if we could train the system to give weightage to people's "awesome" tags
on items, depending upon how much their previous tags are appreciated by other
people.
For example: Let's say you tag a book to be awesome. Now, if 100 other people
check that book in, and (lets say) 80 of them also tag it to be awesome- it
will mean that your opinion matches a majority of people. On the other hand, if
100 other people check that book in and (say) only 5 of them tag it as awesome,
this would mean that your awesome tag is not in coherence with the majority.
So, in the former case, your awesome tag can be given more weightage as
compared to the latter.
Also, the weightage may vary according to genres. So- you may have a good taste
in mystery books but your taste in classical literature might not be the same
as the majority crowd. So- the weightage of your awesome tag in mystery would
be higher than classical literature.
We can even extend it to provide recommendations to users depending on their
coherence with other users with similar taste.
I am looking forward to your suggestions and feedback on this.
Thank you for your time
Vanya
--
Rogan Hamby, MLS, CCNP, MIA
Managers Headquarters Library and Reference Services,
York County Library System
“You can never get a cup of tea large enough or a book long enough to suit me.”
― C.S. Lewis
