Leveraging User Modeling on the Social Web with Linked Data
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Leveraging User Modeling on the Social Web with Linked Data

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Talk give by Ke Tao at #ICWE2012 titled Leveraging User Modeling on the Social Web with Linked Data

Talk give by Ke Tao at #ICWE2012 titled Leveraging User Modeling on the Social Web with Linked Data

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Leveraging User Modeling on the Social Web with Linked Data Leveraging User Modeling on the Social Web with Linked Data Presentation Transcript

  • Leveraging User Modeling on the SocialWeb with Linked Data#ICWE2012 Berlin, Germany, July 27th 2012 Fabian Abel, Claudia Hauff, Geert-Jan Houben, Ke Tao Web Information Systems, TU Delft, the Netherlands Delft University of Technology
  • 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 Leveraging User Modeling on the Social Web with Linked Data 2
  • Kyle Hometown: South Park, Colorado Leveraging User Modeling on the Social Web with Linked Data 3
  • 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. Leveraging User Modeling on the Social Web with Linked Data 4
  • Kyle tweets about his upcoming trip Looking forward to visit Paris next week! Leveraging User Modeling on the Social Web with Linked Data 5
  • 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! Leveraging User Modeling on the Social Web with Linked Data 6
  • Challenges • How to create a meaningful profile that supports the given application? à how to bridge between the Social Web chatter of a user and the candidate items of a recommender system? c9   User  Profile   c6   Applica0on   c1   concept      weight   c5   that  demands  user     interest  profile  regarding   ?   c1                    -­‐concepts   c3   d4   d3   d1   d5  Social Web c2   d2   d3   d4   Candidate Items Recommendations Leveraging User Modeling on the Social Web with Linked Data 7
  • Challenge of Recommending Points of Interests (POIs) to Kyle The astronomer Johannes Vermeer The la cema kerLooking forward to dbpedia:Louvrevisit Paris next week! dbpedia:Paris Leveraging User Modeling on the Social Web with Linked Data 8
  • LOD-based User Modeling concepts  that  can  be  extracted   User  Profile   from  the  user  data     weigh0ng  strategies   concept      weight   c2   c1   c4   c9   0.4   c6   c2   c1   cy   0.1   c5   c3   0.2   cx   …   …   c3   Applica0on   background  knowledge     that  demands  user     user  data   (graph  structures)   interest  profile  regarding  Social  Web                    -­‐concepts   Linked  Data   Leveraging User Modeling on the Social Web with Linked Data 9
  • User Modeling Building Blocks Leveraging User Modeling on the Social Web with Linked Data 10
  • Strategies for exploiting the RDF-based background knowledge graph Indirect Mention Indirect Mention Direct Mention @RDF_statement @RDF_graph dbpedia:Louvre dbpp rop: tags: girl with lo catio pearl earring n geo: The Hague dbpedia:The_Hague A   B   C   …   rd f:type Artifactdbpedia:Girl_with_pearl_earring foaf:maker The The foaf:maker lacemaker astronomer Johannes Vermeer Leveraging User Modeling on the Social Web with Linked Data 11
  • Weighting Scheme User  Profile   concept      weight   c1   374   ?   c2   152   ?   c3   73   ?   …   …   Weighting scheme: count the number of occurrences of a given graph pattern. Leveraging User Modeling on the Social Web with Linked Data 12
  • Source of User Data• Twitter• Flickr Leveraging User Modeling on the Social Web with Linked Data 13
  • 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 Leveraging User Modeling on the Social Web with Linked Data 14
  • Evaluation Leveraging User Modeling on the Social Web with Linked Data 15
  • 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? Leveraging User Modeling on the Social Web with Linked Data 16
  • Dataset users 394 duration 11 months tweets 2,489,088 location 11% pictures 833,441 location 70.6%(within 10km) Leveraging User Modeling on the Social Web with Linked Data 17
  • Dataset Characteristics: Profile Sizes ratio between Flickr-based profile size and Twitter-based profile size 100 size of Flickr-based profile is greater than 10 size of Twitter-based profile 81.4% of the users, 1 Twitter-based profiles are bigger than Flickr-based profiles 0.1 size of Twitter-based profile is greater than 0.01 size of Flickr-based profile Flickr-based profile is 0.001 empty 0 0% 20% 40% 60% 80% 100% user percentiles Leveraging User Modeling on the Social Web with Linked Data 18
  • 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 Leveraging User Modeling on the Social Web with Linked Data 19
  • Results: Impact of User Data Source Selection Combination of Twitter !#+" and Flickr user data!"#$%&%()*+#$,--*,(.*/01#,&2"#* !#*" allows for best !#)" performance !#(" !#" 78$!" !#&" 98$!" !#%" ,8$!" !#$" !" ,-./01" 23.451" ,-./01"6" 23.451" Twitter seems to be more valuable for the given application Leveraging User Modeling on the Social Web with Linked Data 20
  • Results: Impact of Strategies for Exploiting RDF-based Background Knowledge The more background !#," information the better !"#$%&%()*+#$,--*,(.*/01#,&2"#* !#+" the user modeling !#*" !#)" performance !#(" 89$!" !#" !#&" :9$!" !#%" ;9$!" !#$" !" -./012" .4-./012" .4-./012" 304564" 304564"7" 304564"77" Leveraging User Modeling on the Social Web with Linked Data 21
  • Leveraging User Modeling on the Social Web with Linked Data Leveraging User Modeling on the Social Web with Linked Data 13 13Results: Combining different Strategies Table 2. Overview on the di erent strategies for integrating background knowledge. Table 2. Overview on the di erent strategies for integrating background knowledge. Twitter and Flickr are exploited as user data source. Twitter and Flickr are exploited as user data source. Leveraging User Modeling on the Social Web with Linked Data 13 strategy strategy P@10 R@10 F@10 P@10 R@10 F@10 P@20 R@20 F@20 P@20 R@20 F@20 Table 2. Overview on the di erent strategies for integrating background knowledge. core strategies: core strategies: Twitter and Flickr are exploited as user data source. direct mentions direct mentions 0.715 0.260 0.412 0.715 0.260 0.412 0.580 0.179 0.298 0.580 0.179 0.298 strategy indirect mentions I P@10 R@10 F@10 0.699 0.268 0.426 P@20 R@20 F@20 0.566 0.185 0.308 indirect mentions I 0.699 0.268 0.426 0.566 0.185 0.308 indirect mentions II core strategies: 0.475 0.820 0.312 0.727 0.436 0.569 indirect mentions II 0.820 0.312 0.475 0.727 0.436 0.569 direct mentions 0.715 0.260 0.412 combined strategies: 0.580 0.179 0.298 combined strategies: indirect mentions mentions I direct & indirect I 0.699 0.268 0.426 0.733 0.216 0.360 0.566 0.185 0.308 0.608 0.287 0.416 indirect mentions mentions II direct & indirect II 0.820 0.312 0.475 0.836 0.333 0.4975 0.727 0.436 0.569 0.747 0.466 0.596 combined strategies: indirect mentions I + II 0.830 0.325 0.489 0.739 0.456 0.587 direct & indirect mentions I 0.733 0.216 0.360 0.608 0.287 0.416 direct & indirect mentions I + II 0.839 0.337 0.502 0.751 0.473 0.603 direct & indirect mentions II 0.836 0.333 0.4975 0.747 0.466 0.596 indirect mentions I + II 0.830 0.325 0.489 0.739 0.456 0.587 direct & indirect mentions RDF statements from the Linked Open Data Cloud, which does not exploit I + II 0.839 0.337 0.502 0.751 0.473 0.603 the F@20 performance is more than doubled. The consideration of background knowledge obtained from the Linked Open Data Cloud therefore clearly improves Combining all background the performance of user modeling on the Social Web which answers our main which does not exploit RDF statements from the Linked Open Data Cloud, research question. exploitation strategies improves the the F@20 performance is more than doubled. The consideration of background Furthermore, we can answer the specific research questions raised at the knowledge obtained from the Linked Open Data Cloud therefore clearly improves beginning of this section as follows. For the task of recommending points of user modeling performance clearly the performance of user modeling on the Social Web which answers our main interests, it turns out that (1) the aggregation of Twitter and Flickr user data research question. allows for the best user modeling performance and that (2) the user modeling Furthermore, we can answer the specific research questions raised at the quality increases the more background information we consider from the Linked beginning of this section as follows. For the task of recommending points of Open Data Cloud. Finally, (3) the best performance is achieved by combining interests, it turns out that (1) the aggregation of Twitter and Flickr user data the di erent graph patterns for acquiring background information and inferring allows for the best user modeling performance and that (2) the user modeling user preferences. Leveraging User Modeling on the Social Web with Linked Data 22 quality increases the more background information we consider from the Linked Open Data Cloud. Finally, (3) the best performance is achieved by combining 6 Conclusions
  • 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 Leveraging User Modeling on the Social Web with Linked Data 23
  • Thank you! Email: K.Tao@tudelft.nl Twitter: @wisdelft @taubau http://persweb.orgLeveraging User Modeling on the Social Web with Linked Data 24