weaving linked open data into user profiling on the social web
DESCRIPTION
Talk by Ke Tao (from Web Information Systems, TU Delft) at MultiA-Pro Workshop, WWW 2012TRANSCRIPT
DelftUniversity ofTechnology
Weaving Linked Open Data into User Profiling on the Social Web
MultiA-Pro, Lyon, France, April 16th 2012
Fabian Abel, Claudia Hauff, Geert-Jan Houben, Ke TaoWeb Information Systems, TU Delft, the Netherlands
2Weaving Linked Open Data into User Profiling 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
recommending points of interest
3Weaving Linked Open Data into User Profiling on the Social Web
Kyle
Hometown: South Park, Colorado
4Weaving Linked Open Data into User Profiling on the Social Web
Kyle recently uploaded photos to Flickr
During his trip to the Netherlands, he uploaded pictures to Flickr.
tags: delft, vermeergeo: The Hague
tags: girl with pearl earringgeo: The Hague
5Weaving Linked Open Data into User Profiling on the Social Web
Kyle tweets about his upcoming trip
Looking forward to visit Paris next week!
6Weaving Linked Open Data into User Profiling on the Social Web
Interests of Kyle?
tags: delft, vermeergeo: The Hague
tags: girl with pearl earringgeo: The Hague
Looking forward to visit Paris next week!
• Given Kyle’s Flickr and Twitter activities, can we infer Kyle’s interests?
• Knowing that Kyle will visit Paris, France, can we recommend him places that might be interesting for him?
7Weaving Linked Open Data into User Profiling on the Social Web
Challenges• How to create a meaningful profile that supports
the given application? how to bridge between the Social Web chatter of a user and the candidate items of a recommender system?
User Profileconcept weight
?Application
that demands user interest profile regarding
-concepts
c1c5
c6
c9
Social Web d5
c1c3
c2
d1 d2d3 d4
d4 d3
Candidate Items Recommendations
8Weaving Linked Open Data into User Profiling on the Social Web
Challenge of Recommending Points of Interests (POIs) to Kyle
Johannes Vermeer
dbpedia:Louvre
Looking forward to visit Paris next week! dbpedia:Pari
s
The lacemaker
The astronomer
9Weaving Linked Open Data into User Profiling on the Social Web
c1
c4
c5
c6
LOD-based User Modelingweighting strategies
Applicationthat demands user
interest profile regarding -concepts
c2
c3
cx
cy
c9
User Profileconcept weight
0.4
0.1
0.2
c1
c2
c3
……
concepts that can be extracted from the user data
user data
Social Web
background knowledge (graph structures)
Linked Data
10Weaving Linked Open Data into User Profiling on the Social Web
User Modeling Building Blocks
11Weaving Linked Open Data into User Profiling on the Social Web
tags: girl with pearl earringgeo: The Hague
dbpedia:Girl_with_pearl_earring
A
ArtifactB
The lacemaker
C
Theastronomer
…
rdf:type
Johannes Vermeerfoaf:maker
foaf:maker
Strategies for exploiting the RDF-based background knowledge graph
Direct MentionIndirect Mention@RDF_statementIndirect Mention
@RDF_graph
dbpedia:Paris
dbpedia:Louvre dbpprop:locationlocatedIn
13Weaving Linked Open Data into User Profiling on the Social Web
Weighting Scheme
Weighting scheme: count the number of occurrences of a given graph pattern.
User Profileconcept weight
?
?
?
c1
c2
c3
……
User Profileconcept weight
374
152
73
c1
c2
c3
……
14Weaving Linked Open Data into User Profiling on the Social Web
Source of User Data• Twitter
• Flickr
15Weaving Linked Open Data into User Profiling on the Social Web
Mining the Geographic Origins of User Data
• Tweets or Flickr images posted by a GPS-enabled device;
• Images geo-tagged manually on Flickr world map
• Otherwise : exploit the title and the tags the users assign to their images
16Weaving Linked Open Data into User Profiling on the Social Web
Evaluation
17Weaving Linked Open Data into User Profiling on the Social Web
Research Questions
1. How does the source of user data influence the quality in deducing user preferences for POIs?
2. How does the consideration of background knowledge from the Linked Open Data Cloud impact the quality of the user modeling?
3. What (combination of) user modeling strategies allows for the best quality?
18Weaving Linked Open Data into User Profiling on the Social Web
Dataset
users 394
tweets 2,489,088 location 11% pictures 833,441 location 70.6%(within
10km)
duration 11 months
19Weaving Linked Open Data into User Profiling on the Social Web
Dataset Characteristics: Profile Sizes
81.4% of the users, Twitter-based profiles are
bigger than Flickr-based profiles
20Weaving Linked Open Data into User Profiling on the Social Web
Experimental Setup• Task:
= Recommending POIs = Predicting POIs which a user will visit
• Ground truth:• split data into training data (= first nine months) and test data
(= last two months) • POIs that the user visited in the last two months are considered
as relevant• Metrics:
• Precision@k, Recall@k and F-Measure@k: precision, recall and f-measure within the top k of the ranking of recommended items
21Weaving Linked Open Data into User Profiling on the Social Web
Results: Impact of User Data Source Selection
Combination of Twitter and Flickr
user data allows for best performance
Twitter seems to be more valuable for the
given application
22Weaving Linked Open Data into User Profiling on the Social Web
Results: Impact of Strategies for Exploiting RDF-based Background Knowledge
The more background information the better the user
modeling performance
23Weaving Linked Open Data into User Profiling on the Social Web
Results: Combining different Strategies
Combining all background exploitation strategies improves the user modeling performance
clearly
24Weaving Linked Open Data into User Profiling on the Social Web
ConclusionsWhat we did: • LOD-based User Modeling on the Social Web• Different strategies for exploiting RDF-based background
knowledgeFindings:1. Combination of different user data sources (Flickr &
Twitter) is beneficial for the user modeling performance2. User modeling quality increases the more background
knowledge one considers3. Combination of strategies achieves the best
performanceFuture work: •Investigate weighting schemes that weight the different
RDF graph patterns for acquiring background knowledge differently
25Weaving Linked Open Data into User Profiling on the Social Web
Thank you!
Slides : http://goo.gl/Zdg4K
Email: [email protected]: @wisdelft @taubauhttp://persweb.org