Semantic Recommandation Sytems for Research 2.0
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Semantic Recommandation Sytems for Research 2.0

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Semantic Recommandation Sytems for Research 2.0 Semantic Recommandation Sytems for Research 2.0 Presentation Transcript

  • SEMANTIC RECOMMENDATION SYSTEMS FOR RESEARCH 2.0 OR A Conceptual Prototype for a Twitter based Recommender System for Research 2.0 by Patrick ThonhauserThursday, October 11, 12
  • OUTLINE • Motivation • Basics (Semantic Web, Recommender Systems, Natural Language Processing) • Conceptual Prototype • Test results and Discussion • QuestionsThursday, October 11, 12
  • MOTIVATION • Is Twitter useful for discovering new connections between researchers in similar subject areas (and why Twitter)? • How much information can we extract form 140 character strings? • Is it possible to separate useful information from noise? • Are there any appropriate classifiers and metrics to measure the significance of Twitter users and Tweets?Thursday, October 11, 12
  • SEMANTIC WEB • Additional Layer of Information • Linked Data (use URIs as names, use HTTP URIs, use standards to provide Information, include links to other URIs) • RDF (based on triples -> subject, predicate, object) is like HTML for the classic web • Nearly all semantic web standards are based on RDF (like FOAF - Friend of a Friend Project)Thursday, October 11, 12
  • RECOMMENDER SYSTEMS • Collaborative Filtering (user based/ item based) • Content Based Recommendation • Knowledge Based Recommendation • Hybrid RecommendationsThursday, October 11, 12
  • NATURAL LANGUAGE PROCESSING (NLP) • Classification of Microtext Artefacts (This presentation is killer!) • Applying NLP - Pipelines • End of Sentence Detection • Tokenization • POS Tagging • Chunking • ExtractionThursday, October 11, 12
  • THE CONCEPT OF THOUGHT BUBBLES Let’s imagine every Twitter user belongs to several different topic related BubblesThursday, October 11, 12
  • LET’S SUMMARIZE •A user is part of topic related bubbles • Twitter users within topic related bubbles don’t necessarily know each other • Connections of already existing connections of the service user lead to new information • Non bidirectional connections preferred So how can we find such potentially interesting users?Thursday, October 11, 12
  • PROOF OF CONCEPT SYSTEM (1) Preselection of user set, which will be analyzed in depth A USERS THOUGHT BUBBLE SPORTS (2) Apply NLP-Pipeline for measuring user similarity SERVICE IOS DEV USER (3) Categorize the top-n best scoring TW RE ITT SOCIAL MEDIA ST ER T BU HO users according to the idea of AP BB UG I LE HT S AP Thought Bubbles I PRE- FILTERING NLP (4) Recommend top-n best scoring CLUSTERING users of a category to the user DB CATEGORI ANALYZE SATION RECS SERVER (5) Analyze acceptance of recommendationsThursday, October 11, 12
  • (1) PRE-SELECTION/FILTERING Filter accounts Filter accounts where: Filter non that are already follower_count < 300 English speaking connected to you status_count < 1000 accounts Friends of Identifiy People Friends Filter Filter Filter by using a simple Twitter NLP Pipeline Accounts Set of Twitter accounts for further processing • The set of friends of friend‘s Twitter accounts changes from iteration to iteration • Filtersare added after analyzing the acceptance of recommendationsThursday, October 11, 12
  • (2) NLP PIPELINE Tokenization and Neglect 200 most stripping Raw Tweets @mentions and POS tagged Tweets used English words URLs [(The, AT), (grand, JJ), @testuser The (jury, NN), grand jury (commented, commented on a POS tagging VBD), (on, IN), Chunking number of… (a, AT), (number, NN), ... (., .)] Set of Frequency Distributed mined Mined nouns and phrases nouns and phrases [(jury, NN), [(jury, 34), number, (social, 23), Frequency NN), DB (test case, Distribution (social 16), ...] dayly, NP), ...] Filter top n words 400 most recent Tweets of a potential recommendation are used for calculating the similarity measureThursday, October 11, 12
  • • Calculate top-n users by applying Single-Linkage- Clustering • Categorize if user belongs to user specific bubbles • Present recommendation lists to users • Analyze acceptance of recommendations (connect user accounts with FOAF) and add new filter predicate if necessary.Thursday, October 11, 12
  • recommendations are framed @gargamit100* @selvers* @UpsideLearning* @poposkidimitar* @jkalten* SUPERVISED @cpappas* @pfidalgo1* @timbuckteeth* @starsandrobots* @TheJ Russ @cliveshepherd* TEST RUN @Microsoft @jtcobb* @MichaelPhelps @SebastianThrun* @elearning* @elvaandrade @BarackObama @SteveVictor @AnwarRichardson @pabaker55* @jamesmclynn @DrEvanHarris @mstrohm* @AmyFrearson @gekitz @Hhaitch @sclater* @TheRock @MCeraWeakBaby @fatcharlesh @FrankViola @timbarker @AnnaOscarsson @WithDrake sabrinaVanessa @charliesheen @WWEDanielBryan @cmccosky @kaitlyntrigger @judithsei* @atsc* @melaniedaveid @Emmadw* @ladygaga @marcusfairs @lucyheartsTW @PeterSmith @MikeVick @meadd cameron 0 0.075 0.150 0.225 0.300Thursday, October 11, 12
  • UNSUPERVISED TEST RESULTS The probability that a recommended item is relevant is 64.4%. Standard Derivation: 31.5%Thursday, October 11, 12
  • DISCUSSION Twitter IS useful for discovering new information in sense of Research 2.0 but: • Recommendations reflect the Twitter behavior of the user • Automated tweets harm recommendation results (one sentence gets an enormous weight because it occurs very very often) • Twitter‘s request limitation is a show stopper • Comparison to similar systems (Content and collaborative filtering)Thursday, October 11, 12
  • THANK YOU! ANY QUESTIONS?Thursday, October 11, 12