1. GeniUS: Generic User Modeling Library
for the Social Semantic Web
JIST2011, December 2011, Hangzhou, China
Qi Gao, Fabian Abel, Geert-Jan Houben
{q.gao, f.abel, g.j.p.m.houben}@tudelft.nl
Web Information Systems
Delft University of Technology
Delft
University of
Technology

2. What we do: Science and Engineering for the
Personal Web
domains: news social media cultural heritage public data e-learning
Personalized Personalized
Adaptive Systems
Recommendations Search
Analysis and
User Modeling
Semantic Enrichment,
Linkage and Alignment
user/usage data
Social Web
GeniUS: Generic User Modeling Library for the Social Semantic Web 2

3. Motivation
• Sparsity problem
Product • do not have enough useful
Recommender information for a (new) user
? ? • Possible solution: gathering user data
from other sources
User Modeling • But not all data may be relevant for
the given application context.
• how to filter out user data that does
not fit the target application context?
I’m a new user.
Recommend me
some product
GeniUS: Generic User Modeling Library for the Social Semantic Web 3

4. Research Challenges of GeniUS
Various applications in different domains
Product Movie Hotel Product
recommender recommender recommender recommender
Profile
?
customized user
profile construction
Analysis and interested in:
User Modeling
Product Movie location
Semantic Enrichment
How can we build a flexible and extensible
user modeling functionality that adapts to
the demands of a given application context?
GeniUS: Generic User Modeling Library for the Social Semantic Web 4

5. What is GeniUS?
• GeniUS is a topic and user modeling software library that
• produces semantically meaningful profiles to enhance the
interoperability of profiles between applications;
• provides functionality for aggregating relevant information about a
user from the Social Web;
• generates domain-specific user profiles according to the information
needs of different applications;
• is flexible and extensible to serve different applications.
GeniUS: Generic User Modeling Library for the Social Semantic Web 5

6. GeniUS: Generic Topic and User Modeling Library
for the Social Semantic Web
Semantic Web
semantic data
Filter
enriched
user data items user profiles
Item items Weighting RDF
Enrichment
Fetcher Function interested in: Serialization
product location
Social Web
Modeling RDF
Configuration Repository
GeniUS: Generic User Modeling Library for the Social Semantic Web 6

7. Item
Fetcher
GeniUS modules: Item Fetcher and Semantic Enrichment
Enrichment
raw content
a <sioc:Post> ; sioc:has_topic dbpedia:Apple_Inc;
Twitter SpotLight,
dcterms:created … ; sioc:has_topic dbpedia:GarageBand;
API Zemanta,
sioc:has_creator …; sioc:has_topic dbpedia:Ipad;
OpenCalais
sioc:content … .
Social Web
Awesome, love the new
Awesome, love the new Garageband for iPad #apple
Garageband for iPad #apple
dbpedia:GarageBand dbpedia:Ipad dbpedia:Apple_Inc
GeniUS: Generic User Modeling Library for the Social Semantic Web 7

8. Weighting
GeniUS modules: Weighting Function and Function
RDF Serialization
RDF
Serialization
weight
(dbpedia:GarageBand)
weight
(dbpedia:Jazz)
weight
TF (dbpedia:Second_Life)
RDF
TF-IDF
Serialization
Time-sensitive
the weighted
interests vocabulary
GeniUS: Generic User Modeling Library for the Social Semantic Web 8

9. Filter
GeniUS modules: Configuration and Filter Modeling
Configuration
(Jazz, 0.5889)
(Second_Life,
0.4101)
(Second_Life,
0.3114) SELECT DISTINCT ?t WHERE {
Filter
? <rdf:type> <dbpedia-owl:Software> } (GarageBand,
(GarageBand, 0.2158)
0.1638)
enriched
items items
Twitter
SpotLight TF
API
Modeling
Configuration
GeniUS: Generic User Modeling Library for the Social Semantic Web 9

10. GeniUS: Generic Topic and User Modeling Library
for the Social Semantic Web
Semantic Web
GeniUS User Profile Applications
interested in:
product location …
Social Web
How do user profiles generated by GeniUS support
different types of applications?
GeniUS: Generic User Modeling Library for the Social Semantic Web 10

11. Analysis of Domain-specific User Profile Construction
• Dataset
• 72 Twitter users (CS researchers) observed over a period of 6 months
(> 40,000 tweets)
• a variety of topics mentioned in the tweets
• Research questions
• 1. What are the characteristics of (complete) Twitter-based profiles
generated with GeniUS ?
• 2. Can domain-specific profiles be derived from Twitter activities ?
• 3. What are the characteristics of such domain-specific profiles?
GeniUS: Generic User Modeling Library for the Social Semantic Web 11

12. Analysis of Domain-specific User Profile Construction
average number of entities: 1097.1
# of tweets/entites/entity types
10000
tweets
DBPedia entities
entity types
1000
100
10
average number of types: 35.0
0
0 10 20 30 40 50 60 70
users
a potential to generate domain-specific profiles
by categorizing entities according to their types
GeniUS: Generic User Modeling Library for the Social Semantic Web 12

13. Analysis of Domain-specific User Profile Construction
domain: location domain: location
generic domain: entertainment
(all domains) domain: entertainment product
domain: product domain: product
generic: all domains domain specific: products
10000 domain specific: locations sub-domain specific: music products
number of entities
domain specific: entertainment
number of entities
sub-domain specific: books
domain specific: products 1000
sub-domain specific: software products
1000
100
100
10
10
1 1
0 10 20 30 40 50 60 70
0 10 20 30 40 50 60 70
users users
the more specific the domain the smaller the profiles
Are the domain-specific user profiles beneficial for
supporting different recommendation applications?
GeniUS: Generic User Modeling Library for the Social Semantic Web 13

14. Evaluation of Domain-specific User Profile Construction
• Task: Recommending domain-specific tweets
• Domains:
• three domains: location, entertainment, product
• three sub-domains of product: book, software, music
• Recommender algorithm: cosine similarity between profile
and candidate item
• Ground truth: relevant (re-)tweets of users
• Candidate items: all the tweets posted during evaluation
period Recommendations = ?
P(u)= ? user profile
time
1 month
GeniUS: Generic User Modeling Library for the Social Semantic Web 14

15. Evaluation results
the domain-specific user modeling strategies
improve the performance of recommendations
!"('#
!"(&# 8)*),.29#-00#61/-.*4#
61/-.*:45)2.;2#
!"(%#
!"($#
!"(!#
!""#
!"!'#
!"!&#
!"!%# three different domains
!"!$#
!"!!#
)*+),+-.*/)*+# 012-031*4# 5,1672+4#
$%%&'($)*+#,*-$'+#
GeniUS: Generic User Modeling Library for the Social Semantic Web 15

16. Evaluation results
The sub-domain-specific user modeling strategy
also improve the performance of recommendation.
!#&$"
7686-*+9"5::"/.'5*8)"
/.'5*8;),6+*<+"
!#&"
)(1;/.'5*8;),6+*<+"
!#%$"
!""#
!#%" three sub-domains
of product
!#!$"
!"
'()*+",-./(+0)" 1..2)" ).345-6",-./(+0)"
$%%&'($)*+#,*-$'+#
The user modeling quality varies only slightly
between the different domains
GeniUS: Generic User Modeling Library for the Social Semantic Web 16

17. Wrap up
• GeniUS: Generic topic and User modeling library for the Social Semantic Web
• exploits traces (e.g. tweets) that people leave on the Social Web
• enriches the semantics of these traces
• constructs semantic user profiles
 profile construction can be customized and is adapted to a given application context
• Analysis:
• Twitter-based user profiles contain a great variety of topics
• GeniUS succeeds in generating profiles for different applications and domains
• Evaluation:
• domain-specific user modeling strategies (powered by the semantic filtering of
GeniUS) allow clearly for the best performance
• the more GeniUS adapts to the given domain (and application context) the better
the performance
GeniUS: Generic User Modeling Library for the Social Semantic Web 17

18. Thank You!
q.gao@tudelft.nl
Twitter: @qigaosh
Qi Gao
http://wis.ewi.tudelft.nl/tweetum/
http://wis.ewi.tudelft.nl/genius/
GeniUS: Generic User Modeling Library for the Social Semantic Web 18