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Social Computing Research


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MobiSys introductory seminar: people working on social computing

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Social Computing Research

  1. 1. MOBISYS: SOCIAL COMPUTING ucl-cs research nov. 11 2008 format: 4 slides/minutes per person speakers: • Licia Capra • Afra Mashadi • Claudio Weeraratne • Valentina Zanardi • (Sonia Ben Mokhtar) • (Daniele Quercia) • (Neal Lathia)
  2. 2. social computing? collaborative filtering, reputation systems, trust models, content-sharing
  3. 3. mobisys social behaviour pervasive computing research
  4. 4. licia capra [next up: afra]
  5. 5. pervasive computing research [licia capra]
  6. 6. [my research] pervasive (social) computing pervasive (social) computing infrastructure services context-awareness & distributed trust adaptation models discovery and recommender systems composition of services content sharing overlays folksonomy & ontology
  7. 7. [my team] pervasive (social) computing pervasive (social) computing infrastructure services claudio lucia
  8. 8. [future work] i-TOUR: intelligent Transport system for Optimized URban trips TU Eindhoven Magma Srl Cadzow Ltd.
  9. 9. [scenario 1: route to work] How do we get personalised answers to our queries?
  10. 10. [scenario 2: shopping] get me to the closest shop that accepts my credit card where I can buy some food Who is entitled to upload what content into the system?
  11. 11. [future work] Personalised Transport Information System  Trust-based recommender system Reliable & Decentralised Data Collection  Trust-based access control Analysis of the emerging communities of travelers Threat, Vulnerability, and Risk Analysis for use of data TU Eindhoven Magma Srl Cadzow Ltd.
  12. 12. afra mashadi [next up: valentina]
  13. 13. Coping with unwanted content in MANet Vision: -A lot of content is created and shared Challenges: -Users’ interest follows Zipf distribution Afra Mashhadi
  14. 14. Whom to forward to? Avoid forwarding content to people who are not interested: How to know who is interested? How to reach them?
  15. 15. A multi layer approach
  16. 16. Reasoning on mobility Learn regularity of your contacts Decide who should be the next hop based on the probability of meeting And the interest of the relayers
  17. 17. Future work Cooperation Now what if users are selfish!
  18. 18. valentina zanardi [next up: claudio]
  19. 19. Co n t en t n o l o n ger c l a ssi f i ed i n a Problem definition h i er a r c h y wh i c h c a n b e n a vi ga t ed i n o r d er t o f i n d i n t er est i n g c o n t en t • Content overload • Personalization of content: Social tagging behaviour • Efficiently connect users with relevant content within a huge dataset accuracy coverage 19
  20. 20. Problem definition •CiteULike social bookmarking website: •Users have clearly defined interests: they bookmarked a small subset of papers using a small subset of tags! Standard information retrieval system: ? Poor performances for queries that look for medium-to-low popularity content! Accuracy for papers tagged only by a small subset of users for tags used only by a small subset of users, due to the empty overlap between tags Coverage 20
  21. 21. Proposed solution: Social ranking • Social ranking goal: efficiently connect users with relevant content within a huge dataset Accuracy: User similarity Coverage: Tag expansion Activity approach Dictionary approach Similarity of users computed Similarity of tags computed Similarity of tags computed on on tags they used according to their semantic papers they were associated to relationship 21
  22. 22. Future works Improve the efficiency (accuracy, coverage, scalability) of the proposed technique Performance still lowered in accuracy by noise caused by low tagger users (more than 70% of low taggers!) Apply clustering techniques to group users into communities of interest/tags into communities of topics Apply clustering techniques to group only power users (heavy taggers) into communities and to infer the best fitting group for each low tagger user 22
  23. 23. claudio weeraratne [next up: discussion]
  24. 24. Improving Content Searching in Social Tagging Systems How to find exactly what I want? How to locate relevant content? How to discover important items ranked based on my interests Collaborative Filtering How to improve it? Claudio Weeraratne
  25. 25. Goals • Outperform the static model of the system used by Collaborative Filtering • Find more stable algorithm/similarity measures • Point the focus on user's interest and on the concept of community
  26. 26. Analysis AIM: Increasing accuracy and coverage Analyse users' similarity evolution stability of interest between users u u 4 4 u u u u 1 1 2 u u 6 3 6 Analyse users' interest evolution stability of interest per user t t 4 4 u u 1 t 1 t t 6 6 3 t 2
  27. 27. Output Get a interest-based view of the network  Clustering user by interest If exists stability between users over time  Improve similarity method to achieve stability If exists stability in users' interest  Find a time-adaptive similarity measure If users' interest change over time
  28. 28. the future of mobisys seminars: how can we bring our research together?