[OPEN-ILS-GENERAL] Awesome Box Integration

[McCanna, Terran]

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 
tmccanna at georgialibraries.org 

----- Original Message -----
From: "Rogan Hamby" <rogan.hamby at yclibrary.net>
To: "Evergreen Discussion Group" <open-ils-general at list.georgialibraries.org>
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 < vanyajauhal at gmail.com > 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