This presentation was part of 11th Annual NISO-BISG Forum at ALA, Ensuring the Integrated Information Experience, and given by Tim Spalding of Library Thing on June 23, 2017
2. Who am I?
Book-lover, ex-scholar, programmers
LibraryThing (2005)
LibraryThing for Libraries (2007)
TinyCat (2016)
Syndetics Unbound (2016)
3.
4.
5. At the Intersection Of…
Readers
Collectors
Libraries
Academic, Public,
School, "Tiny"
Online booksellers
Bookstores
Publishers
Authors
Also: archives,
scholars, famous
dead people with
books, music and
movie lovers
6. Data is Good
Everyone their data
Every data its glorious purpose
Every data its data that makes it better
7. My Approach to Data Is…
Loving
Respectful
Flexible
Statistical
Optimistic as to what librarians can do…
11. Content Data
Text of book
Samples, quotes, etc.
Tables of contents
Indexes
Word and phrase statistics
In-text references and footnotes
12. Recommendations by bibliographic data alone
Recommendations by subject alone
Recommendations by statistics alone
Recommendations by content alone
One-Legged Stools:
"Recommendations,"
"Similar Books," etc.
23. Recommendations by bibliographic data alone
Recommendations by subject alone
Recommendations by statistics alone
Recommendations by content alone
One-Legged Stools:
Recommendations
27. Solution: Add a leg or two…
Let users act like professionals
Use statistics on classification
28.
29.
30. "Everyone a Librarian"
Improved
Author disambiguation
(1,741,282)
Edition/work control
(5,544,233)
Canonical book titles
Series
Author name variants
Created
Work relationships
(contained in, commentary
on, parody of, etc.)
Awards
Places, characters, events
Author picture
Author information
(education, family,
occupation, nationality, etc.)
31. The Dewmoji !
174.3 = 💭 🚎 🙈 ⚖
1 💭 Philosophy and Psychology
7 🚎 Ethics
4 🙈 Professional Ethics
.3 ⚖ Lawyers
32. "Everyone's a librarian?"
Ha. Add ANOTHER leg.
Librarians at LibraryThing vet USER DATA:
Tag approval
— LibraryThing has 135m tags; 75% belong to 30,000 unique
Series approval
Award approval
Picture approval
Review approval
33. Solution: Add a leg or two…
Let users act like professionals.
Use user statistics on professional
data
Does that classification map to
user/usage data?
34.
35.
36. DDC against
"people who have X have Y"
Clusters well — high "salience"
618.4 — Birthing books
668.1 — Soapmaking
638.1 — Beekeeping
Clusters terribly — low "salience"
All literature in DDC
796.1 — Miscellaneous games
225.6 — New Testament > Hermeneutics, Exegesis
37. How we do Recommendations
Basic Factors
"People who have X have Y
statistics"
Three different statistical
approaches
Shared tags
Reorder and Drop
Ratings
Reviews
User recommendations
User up and down votes
LT Popularity curves
Library popularity curves
Tag "salience"
Tag approval
tag-to-author
Classification systems
Classification salience
Series
Series order
Series-order importance
Author clustering
In-house algorithmic genre
system
Crosswalks from genre to tag, etc.
Final factor: TASTE!
Mix of authors, popularities,
genres, etc.
38.
39. Steal Someone's Leg
Users do stuff to Professional data
Users add and improve bibliographic information
Professionals do stuff to user data
Professional curation of tags, reviews
Professionals pretend to be users
Publishers suggest similar books
40. Random Hortatory Slogans
Use all the data you can
Free your data
Use data by others,
even distant others
Be flexible
Use statistics
Don't be afraid of users
But don't let them run
rampant either…
Cede ground …
… Take ground
Add professional value
to non-professional
data
42. Idea:
What's the best shelf-order system?
Lay out an entire "typical" library in one long line by
classification
Take data on non-library clustering (e.g., people who
have X have Y)
Calculate the average distance you'd have to travel