#SDoW2011 Keynote: User Modeling and Personalization on Twitter
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#SDoW2011 Keynote: User Modeling and Personalization on Twitter

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Keynote at http://sdow.semanticweb.org/2011/

Keynote at http://sdow.semanticweb.org/2011/

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  • large dataset of more than 10 million tweets and 70,000 news articles
  • 1. Translate between “information need” and Twitter vocabulary and 2. compose answer out of several tweets

#SDoW2011 Keynote: User Modeling and Personalization on Twitter #SDoW2011 Keynote: User Modeling and Personalization on Twitter Presentation Transcript

  • User Modeling and Personalizationon TwitterSDoW, ISWC, Bonn, Oct 23, 2011 Fabian Abel Web Information Systems, TU Delft Delft University of Technology
  • #papers that use Twitter datasets 2006 2007 2008 2009 2010 2011 2012 time User Modeling and Personalization on Twitter 2
  • Perspectives on Twitter data Grrrr…that is gr8 http://bit.ly/47gt3 @bob What are Bob’s personal interests? What are his current demands?... User Modeling and Personalization on Twitter 3
  • What we do: Science and Engineering for the Personal Webdomains: news social mediacultural heritage public datae-learning Personalized Personalized Adaptive Systems Recommendations Search Analysis and User Modeling Semantic Enrichment, Linkage and Alignment user/usage data Social Web User Modeling and Personalization on Twitter 4
  • User Modeling Challenge Personalized News Recommender Profile I want my ? personalized news recommendations! Analysis and User Modeling Semantic Enrichment, (How) Linkage and Alignment can we infer a Twitter- based user profile that supports a news recommender? User Modeling and Personalization on Twitter 5
  • QiGao Geert-Jan Houben Ke TaoFabian, Qi, Geert-Jan, Ke: Analyzing User Modeling on Twitterfor Personalized News Recommendations. UMAP 2011User Modeling FrameworkBuilding Blocks for generating valuable user profiles User Modeling and Personalization on Twitter 6
  • User Modeling Building Blocks 1. Temporal Constraints Profile? (a) time period1. Which tweets of concept weightthe user should be analyzed? ? (b) temporal patterns start weekends end Morning: Afternoon: time Night: June 27 July 4 July 11 User Modeling and Personalization on Twitter 7
  • User Modeling Building Blocks 1. Temporal Constraints Francesca T Sport Schiavone 2. Profile Profile? Type concept weight Francesca Schiavone won ? # hashtag-based French Open #fo2010 entity-based T topic-basedFrenchOpen # fo2010 2. What type of concepts should represent “interests”? time June 27 July 4 July 11 User Modeling and Personalization on Twitter 8
  • User Modeling Building Blocks 1. Temporal Constraints Francesca (a) tweet-based Schiavone 2. Profile Profile? Type concept weight FrancescaFrancesca Schiavone won! Schiavone 3. Semantichttp://bit.ly/2f4t7a French Open Enrichment Tennis Francesca wins French Open French Thirty in womens Open (b) further enrichment tennis is primordially old, an age when agility and desire Tennis recedes as the … 3. Further enrich the semantics of tweets? User Modeling and Personalization on Twitter 9
  • User Modeling Building Blocks 1. Temporal Constraints Profile? 2. Profile concept weight Type ?4. How to weight the Francesca Schiavone 4 3. Semantic concepts? French Open 3 Enrichment Tennis 6Concept frequency (TF) 4. WeightingTFxIDF Scheme weight(FrancescaTime-sensitive weight(French Open) weight(Tennis) Schiavone) time June 27 July 4 July 11 User Modeling and Personalization on Twitter 10
  • User Modeling Building Blocks 1. Temporal (a) time period Constraints (b) temporal patterns (a) hashtag-based (b) entity-based 2. Profile (c) topic-based Type 3. Semantic (a) tweet-based Enrichment (b) further enrichment (a) concept 4. Weighting frequency Scheme User Modeling and Personalization on Twitter 11
  • 1. Temporal (a) time period Constraints (b) temporal patterns (a) hashtag-based (b) entity-based 2. Profile (c) topic-based Type 3. Semantic (a) tweet-based Enrichment (b) further enrichment (a) concept 4. Weighting frequency SchemeAnalysisHow do the user modeling building blocks impact the (temporal)characteristics of Twitter-based user profiles? User Modeling and Personalization on Twitter 12
  • Dataset more than: 20,000 Twitter users 2 months Available online:10,000,000 tweets http://wis.ewi.tudelft.nl/umap2011/ Assange, WikiLeaks founder, Julian under arrest in London 75,000 news articles Nov 15 Dec 15 Jan 15 time User Modeling and Personalization on Twitter 13
  • ProfileSize of user profiles Type ~5% of the users entity-based do not make use of hashtags hashtag-based profiles are empty Entity-based user modeling succeeds topic-based for 100% of the hashtag-based users User Modeling and Personalization on Twitter 14
  • SemanticImpact of Semantic Enrichment Enrichment More distinct topics further enrichment further enrichment per profile (e.g. exploiting links) (e.g. exploiting links) More distinct entities per profile Exploiting external resources allows for Tweet-based significantly richer Tweet-based user profiles (quantitatively) entity-based user profiles topic-based user profiles User Modeling and Personalization on Twitter 15
  • TemporalUser Profiles change over time Constraints Hashtag-based profiles # Example: change stronger than d1-distance: entity-based and topic- old new ? based profiles music T difference between current profile and past tennis older the profile The profile the more it differs from football the current profile User Modeling and Personalization on Twitter 16
  • TemporalTemporal patterns of user profiles Constraints2 1. Weekend profiles differ weekday vs. weekend profiles significantly from d1(pweekday, pweekend) weekday profiles 2. the difference is stronger than between day and day vs. night profiles night profiles d1(pday, pnight) topic-based user profiles User Modeling and Personalization on Twitter 17
  • Observations• Semantic enrichment allows for richer user profiles• Profiles change over time: fresh profiles seem to better reflect current user demands• Temporal patterns: weekend profiles differ significantly form weekday profiles User Modeling and Personalization on Twitter 18
  • 1. Temporal (a) time period Constraints (b) temporal patterns (a) hashtag-based (b) entity-based 2. Profile (c) topic-based Type 3. Semantic (a) tweet-based Enrichment (b) further enrichment (a) concept 4. Weighting frequency SchemeEvaluationHow do the user modeling building blocks impact the quality ofTwitter-based profiles for personalized news recommendations?And can we benefit from the findings of the analysis to improverecommendations? User Modeling and Personalization on Twitter 19
  • Twitter-based Profiles for Personalization• Task: Recommending news articles (= tweets with URLs pointing to news articles)• Recommender algorithm: cosine similarity between user profile and tweets 5.5 relevant• Ground truth: re-tweets of users tweets per user• Candidate items: news article tweets posted during evaluation period 5529 candidate news articles Recommendations = ? P(u)= ? time 1 week User Modeling and Personalization on Twitter 20
  • Profile Overview: Type Performance of User Modeling strategies Topic-based strategy improves S@10# significantly Entity-based T strategy improves the recommendation quality significantly (MRR & S@10) User Modeling and Personalization on Twitter 21
  • Impact of Semantic Enrichment Semantic EnrichmentT Tweet-based Further enrichmentFurther semantic enrichment (exploiting links) improves the quality of the Twitter-based profiles! User Modeling and Personalization on Twitter 22
  • Impact of temporal characteristics Temporal Constraints Adapting to temporal context helps? Selection of temporalT start yes weekends end constraints depends on type of user profile. no Recommendations = ? •Topic-based profiles: time adapting to temporal context is beneficial •Entity-based profiles:startcomplete yesT startfresh end long-term profiles perform better no Recommendations = ? complete: 2 months fresh: 2 weeks time User Modeling and Personalization on Twitter 23
  • Observations• Best user modeling strategy: Entity-based > topic-based >hashtag-based• Semantic enrichment improves recommendation quality• Adapting to temporal context helps for topic-based strategy User Modeling and Personalization on Twitter 24
  • 3 Research QuestionsEngineering-UM-Personalization Perspective User Modeling and Personalization on Twitter 25
  • Semantic Web Engineering Perspective on Twitter (and other social) data Model of the application,What is the Applications e.g. news categories …that understand andactual impact of leverage Social Web data sports -> tennismining andintegrating social translatedata on the &application? integrateEvaluate! Mining Semantics Social Web vocabulary, data e.g. Twitter language Social Web # fo2011 User Modeling and Personalization on Twitter 26
  • 1. Search on Twitter Questio n compose answer Answer translate between query and Twitter vocabularyHow can we find “information” in social (micro-)streams? How can personalization help?see also TREC Microblogging Task: http://trec.nist.gov/data/tweets/ User Modeling and Personalization on Twitter 27
  • 2. Re-using Twitter data in other applications Applications …that understand and leverage Social Web data translate & integrate between application and Twitter vocabularyWhat kind of knowledge can we learnfrom users’ micro-blogging activitiesand how can we (re-)use it for whattypes of applications? User Modeling and Personalization on Twitter 28
  • Example: improving product recommendations with Twitter data? dbpedia:Mark_Haddon dbpedia:Dog dbpedia:Food I would never eat dogs! User Modeling and Personalization on Twitter 29
  • 3. Personalization and Serendipity Cross UM dataset: f.abel@tudelft.nl Profile Cross-system UM: get complete picture about a person Reasoning: what type of things Narcissus could surprise and interest a person?How can we balance betweenpersonalization and serendipity? User Modeling and Personalization on Twitter 30
  • Thank you! Twitter: @fabianabel User Modeling and Personalization on Twitter 31