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Weaving Linked Open Data into User Profiling on the Social Web

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Talk by Ke Tao (from Web Information Systems, TU Delft) at MultiA-Pro Workshop, WWW 2012

Talk by Ke Tao (from Web Information Systems, TU Delft) at MultiA-Pro Workshop, WWW 2012

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  • In Web Information Systems Group from TU Delft, we gather user/usage data from social webUpon that…Furthermore, build application for personalized rec, personalized search, and adaptive systems.The domains includes…In this talk, we focus on recommending the points of interest. Before diving into the theoretical model, let’s come to a story.
  • Kyle is a guy from South Park, Colorado.
  • Recently, he travelled to the Netherlands, visited a museum in The Haugue, where he took pictures of two works and uploaded to Flickr.
  • On another platform, he also tweeted about his upcoming trip. So he was planning to visit Paris soon.
  • As we want to build an application for recommending the POIs, the problem here is to infer the personal interests of Kyle, from two sites of the Social Web.
  • …Go back to the example… turn
  • The view of Delft & the girl with pearl earing are works of Johannes Vermeer (a Dutch painter in 17th century). We assume that Kyle like these two work, and will also be interested in other works of Vermeer, like the astronomer and the lacemaker, etc. Explanation of POIs, what do we want to recommendPOIs = The entities with a type of Place on DBpedia, showed in purple in this slides.
  • To tackle these challenges…Find the concepts in LOD
  • Too difficult
  • ----- Meeting Notes (11/4/12 15:37) -----Add more contents
  • For recommending the Point of Interests, we used two social web platforms as sources of user data; one is the most popular microblogging service Twitter, another one is the most popular images sharing site Flickr.
  • Let us first look at the Flickr and Twitter profiles…
  • Might be better to remove the Costs column...?be prepared for the question "How do you know that a user has visited a POI?" -> "POIs that the user tweeted about or took a photo of" 
  • MENTION: this means that the two sources complement each other
  • further explanation of combining different strategies
  • Our framework extracts typed entities from enriched tweets/news and provides strategies for detecting semantic (trending) relationships between entities. We:investigated the precision and recall of the relation detection strategies,analyzed how the strategies perform for each type of relationships andWhich strategy performs best in detecting relationships between entities?Does the accuracy depend on the type of entities which are involved in a relation?How do the strategies perform for discovering relationships which have temporal constraints, and how fast can the strategies detect (trending) relationships?evaluated the quality and speed for discovering trending relationships that possibly have a limited temporal validity.

Transcript

  • 1. Weaving Linked Open Data intoUser Profiling on the Social WebMultiA-Pro, Lyon, France, April 16th 2012 Fabian Abel, Claudia Hauff, Geert-Jan Houben, Ke Tao Web Information Systems, TU Delft, the Netherlands Delft University of Technology
  • 2. What we do: Science and Engineering for the Personal Webdomains: news social media cultural heritage public data e-learning Personalized Personalized Adaptive Systems Recommendations Searchrecommendingpoints of interest Analysis and User Modeling Semantic Enrichment, Linkage and Alignment user/usage data Social Web Weaving Linked Open Data into User Profiling on the Social Web 2
  • 3. Kyle Hometown: South Park, Colorado Weaving Linked Open Data into User Profiling on the Social Web 3
  • 4. Kyle recently uploaded photos to Flickr tags: delft, vermeer with tags: girl geo: The Hague earring pearl geo: The Hague During his trip to the Netherlands, he uploaded pictures to Flickr. Weaving Linked Open Data into User Profiling on the Social Web 4
  • 5. Kyle tweets about his upcoming trip Looking forward to visit Paris next week! Weaving Linked Open Data into User Profiling on the Social Web 5
  • 6. Interests of Kyle?• Given Kyle’s Flickr and Twitter activities, can we infer Kyle’s interests? tags: delft, vermeer with tags: girl geo: The Hague earring pearl• Knowing that Kyle will visit Paris, geo: The Hague France, can we recommend him places that might be interesting for him? Looking forward to visit Paris next week! Weaving Linked Open Data into User Profiling on the Social Web 6
  • 7. 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? c9 User Profile c6 Application c1 concept weight c5 that demands user interest profile regarding ? c1 -concepts c3 d4 d3 d1 d5Social Web c2 d2 d3 d4 Candidate Items Recommendations Weaving Linked Open Data into User Profiling on the Social Web 7
  • 8. Challenge of Recommending Points of Interests (POIs) to Kyle The astronomer Johannes VermeerLooking forward to dbpedia:Louvrevisit Paris next week! dbpedia:Paris Weaving Linked Open Data into User Profiling on the Social Web 8
  • 9. LOD-based User Modeling concepts that can be extracted User Profile from the user data weighting strategies concept weight c2 c1 c4 c9 0.4 c6 c2 c1 cy 0.1 c5 c3 0.2 cx … … c3 Application background knowledge that demands user user data (graph structures) interest profile regardingSocial Web -concepts Linked Data Weaving Linked Open Data into User Profiling on the Social Web 9
  • 10. User Modeling Building Blocks Weaving Linked Open Data into User Profiling on the Social Web 10
  • 11. Strategies for exploiting the RDF-based background knowledge graph Indirect Mention Indirect Mention Direct Mention @RDF_statement @RDF_graph dbpedia:Louvre tags: girl with pearl earring geo: The Hague A B C … dbpedia:Paris Artifactdbpedia:Girl_with_pearl_earring The The lacemaker astronomer Johannes Vermeer Weaving Linked Open Data into User Profiling on the Social Web 11
  • 12. Weighting Scheme User Profile concept weight c1 374 ? c2 152 ? c3 73 ? … … Weighting scheme: count the number of occurrences of a given graph pattern. Weaving Linked Open Data into User Profiling on the Social Web 13
  • 13. Source of User Data• Twitter• Flickr Weaving Linked Open Data into User Profiling on the Social Web 14
  • 14. 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 Weaving Linked Open Data into User Profiling on the Social Web 15
  • 15. Evaluation Weaving Linked Open Data into User Profiling on the Social Web 16
  • 16. Research Questions1. 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? Weaving Linked Open Data into User Profiling on the Social Web 17
  • 17. Dataset users 394 duration 11 months tweets 2,489,088 location 11% pictures 833,441 location 70.6%(within 10km) Weaving Linked Open Data into User Profiling on the Social Web 18
  • 18. Dataset Characteristics: Profile Sizes 81.4% of the users, Twitter-based profiles are bigger than Flickr-based profiles Weaving Linked Open Data into User Profiling on the Social Web 19
  • 19. 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 Weaving Linked Open Data into User Profiling on the Social Web 20
  • 20. Results: Impact of User Data Source Selection Combination of Twitter !#+" 2"#* and Flickr user data !#*" allows for best & +#$, --* ( . * #, & !# " ) performance /01 !# " ( !# " 78 $! " , !#&" 98 $! " !# " %! "#$%% ( )* , 8 $! " !#$" !" , -./01" 23 .4 51" , -./01"6 " 23 .4 51" Twitter seems to be more valuable for the given application Weaving Linked Open Data into User Profiling on the Social Web 21
  • 21. Results: Impact of Strategies for Exploiting RDF-based Background Knowledge The more background !# " , 2"#* information the better !#+" the user modeling & +#$, --* ( . * 1 #, & !#*" !# " ) performance /0 !# " ( 89 $! " , !# " !#&" : 9 $! " ! "#$%% ( )* !# " % ; 9 $! " !#$" !" - ./012" .4- ./012" .4- ./012" 3 04564" 3 04564"7 3 04564"7" " 7 Weaving Linked Open Data into User Profiling on the Social Web 22
  • 22. Results: Combining different Strategies Combining all background exploitation strategies improves the user modeling performance clearly Weaving Linked Open Data into User Profiling on the Social Web 23
  • 23. 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 Weaving Linked Open Data into User Profiling on the Social Web 24
  • 24. Thank you! Slides : http://goo.gl/Zdg4K Email: K.Tao@tudelft.nl Twitter: @wisdelft @taubau http://persweb.orgWeaving Linked Open Data into User Profiling on the Social Web 25