DelftUniversity ofTechnology
Analyzing Cross-System User Modeling on the Social WebICWE, Cyprus, June 22, 2011
Fabian Abel, Samur Araujo, Qi Gao, Geert-Jan HoubenWeb Information Systems, TU Delft
2Analyzing Cross-System User Modeling on the Social Web
PersonalizedRecommendations
Personalized Search Adaptive Systems
What we do: Science and Engineering for the Personal Web
Social Web
Analysis and User Modeling
user/usage data
Semantic Enrichment, Linkage and Alignment
domains: news social media cultural heritage public data e-learning
3Analyzing Cross-System User Modeling on the Social Web
Pitfalls of User-adaptive Systems
System A
time
Hi, I’m your new user. Give me
personalization!
Hi, I have a new-user problem!
profile ?
profile
Hi, I don’t know that your
interests changed!
Hi, I’m back andI have new interests.
System Cprofile
System Dprofile
System Bprofil
e
How can we tackle these problems?
4Analyzing Cross-System User Modeling on the Social Web
User data on the Social Web
Cross-system user modeling on the Social Web
5Challenge the future
Google Profile URI http://google.com/profile/XY
4. enrich data withsemantics
WordNet®
Semantic Enhancement
Profile Alignment
3. Map profiles totarget user model
FOAF vCard
Blog posts:
Bookmarks:
Other media:
Social networking profiles:
2. aggregate public profile
data
Social Web Aggregator
1. get other accounts of user
SocialGraph API
Account Mapping
Aggregated, enriched profile(e.g., in RDF or vCard)
Analysis and user modeling
5. generate user profiles
Interweaving public user data with Mypes
6Analyzing Cross-System User Modeling on the Social Web
In this paper: User Modeling across Twitter, Flickr and Delicious
Twitter and Delicious• 1500 users• 80k + 620k TAS
Flickr and Delicious• 1467 users• 890k + 680k TAS
This is #interesting: http://bit.ly/3gt42f #web
http://claimid.com
websocialmediaidentity
travel, google IO
Twitter Delicious Flickr
Bob
7Analyzing Cross-System User Modeling on the Social Web
Tag-based user profilesTag-based profile of a user u = set of weighted tags:
€
P(u) = {(t,w(u, t)) | t ∈T}
weight indicates to what degreethe user is interested in t
tag of interest
Lightweight weighting scheme:count how often the user applied the tag
8Analyzing Cross-System User Modeling on the Social Web
Characteristics of tag-based profiles
9Analyzing Cross-System User Modeling on the Social Web
Characteristics of tag-based profiles
1. What are the characteristics of the individual tag-based profiles in Twitter, Flickr and Delicious?
2. How do the tag-based profiles of individual users overlap between the different systems?
10Analyzing Cross-System User Modeling on the Social Web
Size of tag-based profiles
Delicious
Flickr
11Analyzing Cross-System User Modeling on the Social Web
Overlap of tag-based profiles
Overlap of tag-based profile is less than 10% for more
than 90% of the users
12Analyzing Cross-System User Modeling on the Social Web
Entropy of Tag-based profilesDeliciousFlickrTwitter
Flickr & DeliciousTwitter & Delicious
Aggregated profiles reveal wrt entropy significantly more
information than the service specific profiles.
where: - p(t) = probability that t occurs in Tu - Tu = tags in user profile P(u)
€
Entropy(Tu) = p(t)∗(−log2(p(t)))t∈Tu
∑
13Analyzing Cross-System User Modeling on the Social Web
Observations• Profile size varies from system to system (e.g. tag-
based Twitter profiles are rather sparse)• Tag-based profiles of an individual user overlap only little (e.g. overlap is less than 10% for more than 90% of the users)
• Entropy of tag-based profiles:Twitter < Flickr < Delicious < aggregated profiles
14Analyzing Cross-System User Modeling on the Social Web
Cross-System User Modeling for Cold-start recommendations
15Analyzing Cross-System User Modeling on the Social Web
Evaluation: Recommending tags / bookmarks
How does cross-system user modeling impact the recommendation quality (in cold-start situations)?
Hi, I’m your new user. Give me
personalization!
profile ?
delicious
Cosin
e-ba
sed
reco
mm
ende
r
tags to exploreWeb sites to
bookmark
profile
profile
Cros
s-sy
stem
user
mod
elin
g
leave-n-out evaluation
user’s tags and bookmarks
Ground truth:
actual tags and bookmarks of the user
16Analyzing Cross-System User Modeling on the Social Web
User Modeling Building Blocks
Profile?1. Source
System A System B
?
tags weightst1t2t3
analyze
enrich t4t5
0.10.10.50.20.1
weight
2. Semantic
Enrichment
3. Weighting Scheme
1. Which tags should be contained in the profile?2. Further enrich/align tags?3. How to weight the tags?
17Analyzing Cross-System User Modeling on the Social Web
User Modeling Building Blocks (in this talk)
1. Source:a) Personal tags from foreign systemb) Popular tags from target system
2. Semantic Enrichment:a) Enrich tags with similar tags (based on Jaro-Winkler similarity)b) Cross-system rules: if tag A was used in foreign system then
add tag B3. Weighting scheme:
a) Personal usage frequency in foreign systemb) Global usage frquency in target system
profile profile ?
Foreign: Target:
requires profile to compute recommendations
web
blog jav
a
a) simJaro(blog, blogs) is high
blogs
b) Cross-system rule: blogforeign nikontarget
france
personalpopular
similaritycross rules
personalglobal
18Analyzing Cross-System User Modeling on the Social Web
Cross-System User Modeling for Cold-start recommendations
1. Which user modeling strategies performs best in which context?
2. How do the different building blocks of the user modeling strategies (e.g. source of user data) influence the quality of the tag-based profiles?
19Analyzing Cross-System User Modeling on the Social Web
Tag recommendations: Twitter / Delicious
As you can easily see…:-)
20Analyzing Cross-System User Modeling on the Social Web
Tag recommendations: Twitter Delicious
profile profile ?
Improvement regarding P@10, but “global Delicious trend”
performs better regarding MRR & S@1.
popularglobal global
personal
similarityglobal
personal
baseline
personalpersonal
Cross-system user modeling
Cross-system strategies lead to significant improvement
(impact of semantic
enrichment is rather low)Significant improvements
regarding all metrics!
profile
user’s tags global
tag frequencies (weights)profile
user profile
21Analyzing Cross-System User Modeling on the Social Web
Tag recommendations: Delicious Twitter
profile profile ?
Significant improvements regarding all metrics!
user’s tagsand tag frequencies (weights)
user profile
profile
profile
Tag-based profile
information from Delicious seems to be
more valuable than hashtga-based Twitter
profiles
Semantic enrichment (cross-system rules) allow
for significant improvement
regarding P@10
popularglobal
baseline
personalpersonal
Cross-system user modelingglobal
personal
cross rules
globalpersonal
22Analyzing Cross-System User Modeling on the Social Web
Tag Recommendations: different settings
Cross-system user modeling is also beneficial for cold-start tag recommendations in Flickr.
profile ?
target:profile
Cross-system user modeling allows for cold-start tag recommendations in Delicious: Twitter profiles are more appropriate than Flickr profiles.
profile
profile ?profile
target:
1. Cross-system user modeling has significant impact on the recommendation performance
2. To optimize the performance one adapt to the given application setting
23Analyzing Cross-System User Modeling on the Social Web
Bookmark Recommendations
baseline Cross UM Cross UM
1. Cross-system user modeling achieves also significant improvements for cold-start bookmark recommendations
2. Twitter is again a more appropriate source than Flickr
24Analyzing Cross-System User Modeling on the Social Web
Conclusions1. Characteristics of distributed tag-based profiles:
• Overlap of tag-based profiles, which an individual user creates at different services, is low
• Aggregated profiles reveal significantly more information (regarding entropy) than service-specific profiles
2. Performance of cross-system user modeling for cold-start recommendations:• Cross-system UM leads to tremendous (and significant)
improvements of the tag and bookmark recommendation quality
• To optimize the performance one has to adapt the cross-system strategies to the concrete application settinghttp://persweb.org
25Analyzing Cross-System User Modeling on the Social Web
Thank you!
Fabian Abel, Qi Gao, Geert-Jan Houben, Ke Tao
Twitter: @perswebhttp://persweb.org
Datasets: http://wis.ewi.tudelft.nl/icwe2011/um/
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