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PhD Defense - A Context Management Framework based on Wisdom of Crowds for Social Awareness Applications

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These are the slides I presented at INSA de Lyon for my PhD defense, on the 14/10/2010.

These are the slides I presented at INSA de Lyon for my PhD defense, on the 14/10/2010.


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  • w t,d is the number of people p who annotated a given resource d using the term t .
  • Counts the occurrences of each term that is semantically identified in the document’s content.
  • Transcript

    • 1. A Context Management Framework based on Wisdom of Crowds for Social Awareness applications Adrien JOLY PhD Candidate, supervisor: Prof. Pierre MARET, LaHC CIFRE: Alcatel-Lucent Bell Labs France + INSA-Lyon, LIRIS, UMR5205
    • 2. Un cadre de Gestion de Contextes fondé sur l’Intelligence Collective pour améliorer l’efficacité des applications de Communication Sociale Adrien JOLY CIFRE: Alcatel-Lucent Bell Labs France + INSA-Lyon, LIRIS, UMR5205 Encadré par: Prof. Pierre MARET (LaHC), Johann Daigremont (ALBLF)
    • 3.
      • Context — Problem and state-of-the-art
      • Approach — Context-based filtering of social streams
      • Framework — Context tag clouds
      • Evaluation — Perceived relevance
      • Conclusion & future work
      Agenda of this presentation
    • 4. Context Approach Framework Evaluation Conclusion A PhD thesis in a moving industrial research lab
      • 2003-2006: INSA Lyon + QUT Brisbane
      • 2007-2008: Ambient Services Group, ALU Research & Innovation
        • “ Study of semantic models in context-aware software”
        • Projects: Context Services, EASY Interactions, IMS-based applications
      • 2008-2010: Social Communications Dept., ALU Bell Labs France
        • “ Context-aware Social Networking”
        • Team projects: Hyperlocal Social Networks, Multimodal Support…
        • Strategic projects: Environment as a Service , One Million Conversations
      • With support from colleagues:
        • Service Infrastructure Research Domain (SIRD), ALU Bell Labs France
        • LIRIS (DRIM), INSA Lyon
    • 5. Context Approach Framework Evaluation Conclusion Introduction to social networks and information overload “ Aware” user
      • Social updates
      Activities / Status Updates / Contacts
    • 6. Context Approach Framework Evaluation Conclusion Introduction to social networks and information overload “ Aware” user Activities / Status Updates / Contacts
      • Social updates
      Information overload* * aka Cognitive Overflow Syndrome [Lahlou 1997]
    • 7. Context Approach Framework Evaluation Conclusion Introduction to social networks and information overload Filter “ Aware” user Activities / Status Updates / Contacts Needed
      • Social updates
      and productive
    • 8. Context Approach Framework Evaluation Conclusion Problem statement
        • Goal
        • Maintain awareness, reduce information overload
        • Proposition
        • Filter social feeds, recommend most relevant social updates
        • Constraints & requirements
          • Privacy: control in the hands of users
          • Poor context: social updates are short by nature
          • No training is possible: new meaning can emerge anytime
    • 9. Context Approach Framework Evaluation Conclusion State of the Art: Computer-Supported Collaborative Work
        • Fundamental CSCW concepts [Dourish 1992]
          • Awareness:
            • understanding of the activities of others, which provides a context for your own activity
          • Context :
            • object of collaboration, and the way in which the object is produced
            • used to ensure that individual contributions are relevant to the group’s activity
          • Shared feedback
            • presenting feedback on individual users’ activities within the shared [work]space
          • Similar concept on today’s social networks: newsfeed
          • Link between interaction context and relevance
        • Peripheral awareness / Notification systems
          • CANS [Amelung 2005]: activity-based notifications in Sakai CMS  a broader context must be taken in account
        • Social translucence: making activities visible [Erickson 2000]
          • From Knowledge Management to Knowledge Communities
          • Importance of conversations: e.g. give credit
    • 10. Context Approach Framework Evaluation Conclusion State of the Art: content-based filtering
        • Content-based filtering…
          • Recommend items which content match users’ preferences/profile
        • … for social matching:
          • Recommend web pages accordingly to evolving profile [Balabanovic 1997]
          • … or similarity of content with manipulated documents’ [Budzik 2000]
          • Extract context features from software-based activities [Dragunov 2005]
          • Multidimensional activity/context model for notes [van Kleek 2009]
          • Evolving user profile, learnt from implicit content rating [My6sense.com]
    • 11. Context Approach Framework Evaluation Conclusion State of the Art: collaborative filtering
        • Collaborative filtering…
          • Find items that were selected by same users
          • Find people who selected similar items
        • … for social matching
          • “ SoMeONe” [Agosto 2005]: items = topics extracted from web bookmarks
            • Using categories from DMOZ/ODP (static)
          • “ groop.us” [Bielenberg & Zacher 2005] items = tags clusters from Delicious bookmarks
            • Insufficient features from Delicious for recommending relevant items
          • ContactRank [Delalonde 2007]
            • Closed vocabulary is too strict
          • Tags are good features to consider [Hotho 2006, Niwa 2006]
    • 12. Context Approach Framework Evaluation Conclusion Proposal: context-aware filtering
        • C ollaborative filtering via content [Pazzani 1999]
        • Proposal: Hybrid filtering based on context features
      • Publication :
      • Context-Awareness, the Missing Block of Social Networking , International Journal of Computer Science and Applications 2009.
      Java Dev I/O PHP Similar contexts Physical, virtual and social sensors Open set of context features User’s activity Recommend social updates
    • 13. Context Approach Framework Evaluation Conclusion Research issues
        • Previous work
          • Context knowledge useful to determine relevance of shared interactions
          • Context can be extracted from content of users’ tasks
          • Problems: closed vocabularies, learning phases, lack of metadata…
        • Proposed contribution
          • Apply collaborative filtering via content , with crowd-sourced metadata
        • Research questions
          • How to model context knowledge as features for filtering social updates?
          • How does contextual similarity perform as a criteria for recommending relevant social updates, from users’ point of view?
          • How do crowd-sourced metadata compare with content-based features?
    • 14. Agenda of this presentation
      • Context
      • Approach
      • Framework
      • Evaluation
      • Conclusion
    • 15. Context Approach Framework Evaluation Conclusion Similarity of context, our hypothesis
        • C A is the context of a user U A sharing a piece of information I A .
        • C X is the context of a user U X that is a potential recipient of this information.
      Hypothesis: I A is relevant to U X if C A is similar to C X A A = Travel in Asia U A = Alice I A = « Check out my amazing picture ! » A B = Working Java U B = Bob I B = « What database should I use ? » A C = Browsing map U C = Christine I C = « Looking for holiday locations… »
    • 16. Context Approach Framework Evaluation Conclusion Similarity of context, our hypothesis
        • C A is the context of a user U A sharing a piece of information I A .
        • C X is the context of a user U X that is a potential recipient of this information.
      Hypothesis: I A is relevant to U X if C A is similar to C X C A = Travel, Asia C C = Travel C B = Java Dev. A A = Travel in Asia U A = Alice A B = Working Java U B = Bob I B = « What database should I use ? » A C = Browsing map U C = Christine I C = « Looking for holiday locations… » Similar context: travel No relevant match for this context I A = « Check out my amazing picture ! »
    • 17. Context Approach Framework Evaluation Conclusion What is context ?
      • Context [Dey, 2001] :
      • «  any information that can be used to characterize the situation of an entity  »
        • From physical sensors:
        • From computer-based actions:
      Location Surrounding people Other sensors Communication history Web browsing history Document history
    • 18. Context Approach Framework Evaluation Conclusion From physical context sensors to applications – usual approach Context sensors Applications Interpretation Acquisition db
      • Usual representation scheme for context information:
      • Ontology-based
      • / semantic
        • Interoperability
        • Requires ont. modeling
        • Lack of semantic data
        • Scalability issues
      • Publication :
      • Context-Aware Mobile and Ubiquitous Computing for Enhanced Usability: Adaptive Technologies and Applications (IGI Global , 2009)
      Context Management Framework
    • 19. Context Approach Framework Evaluation Conclusion From physical, virtual and social context sensors to applications – our approach Context Management Framework Context sensors Social Applications Interpretation Acquisition db
      • Proposed representation scheme for context information:
      • Contextual tag clouds
        • Machine-interpretable
        • Crowd-sourced model
        • Easy to edit
        • Emergent meaning
      Updates Paris Notre-Dame Café Cloudy Crowded Sitting with:Pierre
    • 20. Context Approach Framework Evaluation Conclusion Our approach, the “Big Picture”
      • Christine’s contextual cloud:
      • McDonalds Chatelet
      • DriveIn Radio Alice France
      • Californication Paris
      • RedHotChiliPeppers Wifi_SSID_5874
      Mario is shopping near Chatelet Mario: «  let’s grab a coffee at SB !  » Alice has just shared a photo [ view ] Lucie is listening to Californication [ i like this ] Kevin: «  new McChicken is great!  » Car incident 32 meters away Alice GPS Wifi McDonald’s restaurant Radio currently playing a song Christine
    • 21. Agenda of this presentation
      • Context
      • Approach
      • Framework
      • Evaluation
      • Conclusion
    • 22. Context Approach Framework Evaluation Conclusion Context Aggregation and Filtering process Social updates Aggregator Sniffers Notifier Filter User Actions and tags Contextual clouds Notifications Context Interfaces Abstraction and weighting Services
    • 23. Context Approach Framework Evaluation Conclusion Considering a first context dimension: browsing activity Social updates Aggregator Sniffers Notifier Filter User Actions and tags Contextual clouds Notifications Context Interfaces Abstraction and weighting Services
      • Publication :
      • “ Workspace Awareness without Overload” Smart Offices and Other Workspaces, Intelligent Environments 2009
    • 24. Context Approach Framework Evaluation Conclusion How to synthesize the contextual tag cloud from web browsing ? The user opens a web page…
    • 25. Context Approach Framework Evaluation Conclusion How to synthesize the contextual tag cloud from web browsing ? Low level and static author description Automatic content analysis Mining semantic concepts from content People-entered tags (wisdom of crowds) 1) URL is sent to the Context Aggregator 2) Content is analyzed by enhancers (including web services)
    • 26. Context Approach Framework Evaluation Conclusion Contextual Tag Clouds, vector space model and algebra Sample tag cloud R : (normalized)
      • Aggregation of a set V of normalized Tag Clouds
      •  normalized sum:
      • Relevance of Tag Cloud R with S
      •  cosine similarity:
      0.1 0.1 0.3 0.5 « Discount » « Flight » « Asia » « Travel »
    • 27. Context Approach Framework Evaluation Conclusion Contextual Tag Clouds, extraction and enhancement functions
      • 1. Extracting weighted terms from: Resource Metadata
      Title Keywords Description Parameters = 50 = 10 = 1
    • 28. Context Approach Framework Evaluation Conclusion Contextual Tag Clouds, extraction and enhancement functions
      • 2+3. Extracting weighted terms from:
      2. Search Query ambient, awareness 3. Resource Location video, all, alcatel-Lucent
    • 29. Context Approach Framework Evaluation Conclusion Contextual Tag Clouds, extraction and enhancement functions
      • Extracting weighted terms from: Social Annotations
      w poster = 11, w work = 11, w gtd = 10, w done = 10, w inspiration = 7, …
    • 30. Context Approach Framework Evaluation Conclusion Contextual Tag Clouds, extraction and enhancement functions
      • Extracting weighted terms from: Semantic Analysis of content
      MIT, Tim Berners-Lee, …
    • 31. Context Approach Framework Evaluation Conclusion Context Aggregation and Filtering process –- in the enterprise Social updates Aggregator Sniffers Notifier Filter User Actions and tags Contextual clouds Notifications Context Interfaces Abstraction and weighting Services
    • 32. Context Approach Framework Evaluation Conclusion Implementation Firefox extension (Javascript) to track web browsing Windows daemon (C++) to track opened PDF documents Lightweight HTTP Server (Java) + 5 tag extractors (Java) incl. 2 web service wrappers Jetty-based HTTP Server (Java) DWR for server-push (Java) Off-line scripts (Java+shell) Firefox sidebar (HTML+Javascript) Mobile application (Java for android) Aggregator Sniffers Notifier Filter Social updates User Actions and tags Contextual clouds Notifications Context Interfaces Abstraction and weighting Services
    • 33. Context Approach Framework Evaluation Conclusion Demonstration of the ECN prototype
      • Demonstration :
        • Enterprise Contextual Notifier, Contextual Tag Clouds towards more Relevant Awareness . CSCW 2010, the ACM Conference on Computer Supported Cooperative Work (ACM, 2010)
    • 34. Agenda of this presentation
      • Context
      • Approach
      • Framework
      • Evaluation
      • Conclusion
    • 35. Context Approach Framework Evaluation Conclusion Requirements and plan
      • Hypothesis:
        • Recommended social updates are relevant when users’ contexts are similar
      • To evaluate:
        • Relevance of social updates based on contextual similarity
        • Relevance of social updates to the context of their posting
        • Differences between context from virtual and social sensors
      • Experimentation plan:
      (1 week) 2 personalized surveys per user Indexing as tags clouds & Matching contexts
    • 36. Context Approach Framework Evaluation Conclusion From browsing activity to social matching Temporal indexing period = 10 mn. Common tags: JAVA, DEV Common tags: TRAVEL  Recommend u5’s social update to u1  Recommend u3’s social update to u7
    • 37. Context Approach Framework Evaluation Conclusion 1. Relevance of social updates based on contextual similarity Matching
    • 38. Context Approach Framework Evaluation Conclusion Survey #1
      • … and 3 social updates with various relevance scores, for each context
      1 2 3 4 1 2 3 4
      • Survey #1 : For each user, 5 personal contextual clouds are proposed…
    • 39. Context Approach Framework Evaluation Conclusion Survey #1 results 1/2
      •  rarity of good matches
      • (few participants  few common tags)
    • 40. Context Approach Framework Evaluation Conclusion Survey #1 results 2/2
      •  Accuracy = 72% (based on MAE between relevance scores and ratings)
      Accuracy
    • 41. Context Approach Framework Evaluation Conclusion 2. Relevance of social updates to the context of their posting
    • 42. Context Approach Framework Evaluation Conclusion Survey #2
      • Survey #2 : For each user’s social update, Evaluation of relevance between social updates and context of posting
      • Results
      • Average relevance rating: 50.3% (over 59 social updates), including: - 71% for social bookmark notifications - 38% for tweets ( ≈ 41% of “me now” statuses on twitter [Naaman’2010])
      • Publication :
        • Contextual Recommendation of Social Updates, a Tag-based Framework . International Conference on Active Media Technology (Springer, 2010)
    • 43. Context Approach Framework Evaluation Conclusion 3. Differences between context from virtual and social sensors Combining virtual and social sensors: good compromise between quantity and quality of matches 280k Number of matches 40k 170k 130k 70k 10k Low precision matches High precision matches
    • 44. Agenda of this presentation
      • Context
      • Approach
      • Framework
      • Evaluation
      • Conclusion
    • 45. Context Approach Framework Evaluation Conclusion Conclusion
        • Goal :
        • Maintain awareness, reduce information overload
        • Previous work :
          • Recommendation of documents and people, based on content of browsed documents
          • Problems: closed vocabularies, learning phases, lack of metadata…
        • Hypothesis :
          • A social update is relevant to a person, if this person’s context is similar to the context of the sender at the time of posting
        • Proposition :
          • Aggregate context as tags, from virtual and social sensors, for ranking social updates
    • 46. Context Approach Framework Evaluation Conclusion Contributions and findings
        • Contributions
          • A tag-based context management framework, and software implementation
          • A working social awareness application that recommends relevant updates
          • A methodology, and tools to evaluate the performance of such applications
        • Findings (using web browsing activity as context) :
          • Encouraging results: 72% accuracy
          • Half social updates are relevant to web browsing context, depending on nature
        • Ready for integration in Bell Labs research projects
          • As a working Social Radar implementation for “ One Million Conversations”
          • As a context management system for “ Environment as a Service”
    • 47. Context Approach Framework Evaluation Conclusion Future work
        • Quality of contextual tag clouds, to be improved
          • Semantic analysis, clustering, and filtering of tags
          • Dynamic weights (based on time)
        • Additional context sensors, to be supported
          • E.g. leverage physical sensors and geo-localized social streams
        • Context tag cloud manipulation interface, to be studied
          • Add graphical representations of tags
          • Multidimensional/hierarchical tag clouds
          • Edit tags and their weights
    • 48. www.alcatel-lucent.com Thank you for your attention! Merci pour votre attention !