tags vs shelves: from social tagging to social classification
TRANSCRIPT
Tags vs Shelves:From Social Tagging to Social Classification
Hypertext 2011
Arkaitz Zubiaga, Christian Korner, Markus Strohmaier
UNED (Madrid, Spain)&
Graz University of Technology (Graz, Austria)
June 8th, 2011
Motivation
Index
1 Motivation
2 User Behavior Measures
3 Experiments
4 Results
5 Conclusions & Outlook
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Motivation
Book Cataloging
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Motivation
Book Cataloging
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Motivation
Book Cataloging
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Motivation
Book Cataloging
Librarians have been cataloging books for centuries.
The task of manually cataloging books becomes very expensive andeffortful for large collections.
For instance, the Library of Congress reported an average cost of $94.58for cataloging each book in 2002 (291,749 books, total: $27.5 million)
Given the enormous costs and efforts required for the task, research ismoving towards automatic classification.
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Motivation
Automatic Classification of Books
Problem: it is not easy to get data representing the aboutness of thebooks.
In addition, content of books is not always available digitally.
Solution:
Social tags provided by users have shown to be helpful (Zubiaga et al,2009)1.Social tagging sites like LibraryThing and GoodReads are gatheringvast amounts of tag annotations on books.
1A. Zubiaga, R. Martınez, V. Fresno. Getting the Most Out of Social Annotations for Web Page Classification. DocEng
2009.
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Motivation
Tagging
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Motivation
Social Tagging
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Motivation
Social Tagging
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Motivation
Problem Statement
Can we find a type of user whose tags further resemble the categorizationby experts?Can we characterize those users?
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Motivation
User Behavior
Korner et al.2 suggested and described the existence of two kinds ofuser behavior: Categorizers and Describers.
Categorizer DescriberGoal of Tagging later browsing later retrievalChange of Tag Vocabulary costly cheapSize of Tag Vocabulary limited openTags subjective objective
Previous works suggest that Describers rather help infer semanticrelations among tags.
Our goal is to discover whether this kind of tagging behavior affectsthe usefulness of tags as to the social classification of books.
2C. Korner. Understanding the Motivation behind Tagging. Hypertext 2009.
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Motivation
User Behavior
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Motivation
User Behavior
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User Behavior Measures
Index
1 Motivation
2 User Behavior Measures
3 Experiments
4 Results
5 Conclusions & Outlook
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User Behavior Measures
User Behavior Measures
Tags per Post (TPP) – Verbosity
TPP(u) =
r∑|Tur ||Ru|
(1)
Orphan Ratio (ORPHAN) – Diversity
n =
⌈|R(tmax)|
100
⌉(2)
ORPHAN(u) =|T o
u ||Tu|
,T ou = {t||R(t)| ≤ n} (3)
Tag Resource Ratio (TRR) – Verbosity + Diversity
TRR(u) =|Tu||Ru|
(4)
C. Korner, R. Kern, H.-P. Grahsl, and M. Strohmaier. Of categorizers and Describers: an evaluation of quantitative measures fortagging motivation. Hypertext 2010.
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User Behavior Measures
Computing measures
These 3 measures provide a weight for each user.
These weights enable to infer a ranking of users according to eachmeasure.
From these rankings, we choose subsets of users as extremeCategorizers (highest-ranked) and extreme Describers (lowest-ranked).
Subsets range from 10% to 100%, with a step size of 10%.
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User Behavior Measures
Book Cataloging
We select subsets of users according to number of tag assignments.
Selecting by percents of users would be unfair, since it would providedifferent amounts of data.
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User Behavior Measures
Objective
We aim at analyzing whether:
Categorizers provide tags that further help infer categorizationperformed by experts.Describers provide tags that further resemble book descriptions.
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Experiments
Index
1 Motivation
2 User Behavior Measures
3 Experiments
4 Results
5 Conclusions & Outlook
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Experiments
Datasets
Set of 38,149 popular books, with categorization data made byexperts:
27,299 categorized according to DDC (10 categories).24,861 categorized according to LCC (20 categories).
Tagging data from 153k+ users on LibraryThing and 110k+ users onGoodReads (100+ users annotated each book).
Additional descriptive data:
Book synopses (Barnes&Noble).User reviews (LibraryThing, GoodReads, and Amazon.com).Editorial reviews (Amazon.com).
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Experiments
Tag-based Book Classification
Software: Multiclass Support Vector Machines (svm-multiclass3).
Vectorial representation of books, using tag frequency values.
We perform 6 different training set selections of 18,000 books, andshow the average accuracy.
Accuracy: #correctguesses#testset .
3http://svmlight.joachims.org/svm multiclass.htmlZubiaga, Korner, Strohmaier () Tags vs Shelves June 8th, 2011 22 / 31
Experiments
Descriptiveness of Tags
Vectorial representation of books (Tr ), using tag frequency values.
Vectorial representation of books (Rr ), using term frequency valueson descriptive data (synopses, reviews).
Cosine similarity between Tr and Rr :
similarityr = cos(θr ) =Tr · Rr
‖Tr‖‖Rr‖=
n∑i=1
Tri × Rri√∑ni=1 (Tri )2 ×
√∑ni=1 (Rri )2
(5)
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Results
Index
1 Motivation
2 User Behavior Measures
3 Experiments
4 Results
5 Conclusions & Outlook
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Results
Results
GoodReads LibraryThing
TPP (verb.) TRR (div.) ORP. (verb. + div.) TPP (verb.) TRR (div.) ORP. (verb. + div.)
Cla
ssifi
cati
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Des
crip
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s
1 TPP measure: Categorizers outperform Describers for classification.2 All the measures (though especially TRR): Describers further
resemble descriptive data.
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Results
Results
GoodReads LibraryThing
TPP (verb.) TRR (div.) ORP. (verb. + div.) TPP (verb.) TRR (div.) ORP. (verb. + div.)
Cla
ssifi
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Des
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3 Verbosity helps find extreme Categorizers.Users who think of a specific shelf to place the book tend to assign atag identifying the shelf.
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Results
Results
GoodReads LibraryThing
TPP (verb.) TRR (div.) ORP. (verb. + div.) TPP (verb.) TRR (div.) ORP. (verb. + div.)
Cla
ssifi
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Des
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4 Diversity does not work to find Categorizers on GoodReads.GoodReads suggests previously used tags to the user, so that it affectsdiversity of tags.
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Results
Results
GoodReads LibraryThing
TPP (verb.) TRR (div.) ORP. (verb. + div.) TPP (verb.) TRR (div.) ORP. (verb. + div.)
Cla
ssifi
cati
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Des
crip
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5 Users providing non-descriptive tags (i.e., different from Describers)produce more accurate classification.
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Conclusions & Outlook
Index
1 Motivation
2 User Behavior Measures
3 Experiments
4 Results
5 Conclusions & Outlook
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Conclusions & Outlook
Conclusions & Outlook
Social classification of books with tagging data, discriminatingextreme Categorizers and Describers.
It complements previous research by showing that users so-calledCategorizers produce more accurate classification.
Non-verbose, non-descriptive, shelf-driven tagging produces moreaccurate classification of books.
Outlook: Further analyzing tagging behavior to find: generalists(users who provide general tags), and specialists (users who providemore specific tags rather focused on the subject).
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Conclusions & Outlook
Thank You
Achiu Arigato Danke Dhannvaad Dua Netjer en ek Efcharisto
Gracias Gracies Gratia Grazie GuishepeliHvala Kiitos Koszonom Merce Merci Milaesker Obrigado Shukran Shukriya Tack Tak Takk
Tanan Tapadh leat Tesekkur ederim Thankyou Toda
E-mail: [email protected]
@arkaitz
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