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

  • 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
  • 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)
    • 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
  • 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
  • Context Approach Framework Evaluation Conclusion Introduction to social networks and information overload “ Aware” user
    • Social updates
    Activities / Status Updates / Contacts
  • 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]
  • Context Approach Framework Evaluation Conclusion Introduction to social networks and information overload Filter “ Aware” user Activities / Status Updates / Contacts Needed
    • Social updates
    and productive
  • 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
  • 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
  • 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]
  • 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]
  • 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
  • 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?
  • Agenda of this presentation
    • Context
    • Approach
    • Framework
    • Evaluation
    • Conclusion
  • 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… »
  • 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 ! »
  • 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
  • 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
  • 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
  • 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
  • Agenda of this presentation
    • Context
    • Approach
    • Framework
    • Evaluation
    • Conclusion
  • 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
  • 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
  • Context Approach Framework Evaluation Conclusion How to synthesize the contextual tag cloud from web browsing ? The user opens a web page…
  • 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)
  • 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 »
  • 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
  • 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
  • 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, …
  • Context Approach Framework Evaluation Conclusion Contextual Tag Clouds, extraction and enhancement functions
    • Extracting weighted terms from: Semantic Analysis of content
    MIT, Tim Berners-Lee, …
  • 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
  • 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
  • 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)
  • Agenda of this presentation
    • Context
    • Approach
    • Framework
    • Evaluation
    • Conclusion
  • 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
  • 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
  • Context Approach Framework Evaluation Conclusion 1. Relevance of social updates based on contextual similarity Matching
  • 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…
  • Context Approach Framework Evaluation Conclusion Survey #1 results 1/2
    •  rarity of good matches
    • (few participants  few common tags)
  • Context Approach Framework Evaluation Conclusion Survey #1 results 2/2
    •  Accuracy = 72% (based on MAE between relevance scores and ratings)
    Accuracy
  • Context Approach Framework Evaluation Conclusion 2. Relevance of social updates to the context of their posting
  • 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)
  • 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
  • Agenda of this presentation
    • Context
    • Approach
    • Framework
    • Evaluation
    • Conclusion
  • 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
  • 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”
  • 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
  • www.alcatel-lucent.com Thank you for your attention! Merci pour votre attention !