Brief presentation  of my previous work Nicolas Maisonneuve – Associate Researcher at Sony CSL Blog:  http://nico.maisonne...
My current & new Interest <ul><li>Human/Organization sciences + computer science </li></ul><ul><li>(e.g. the new interdisc...
My previous project: Atgentive <ul><li>European project:  2-year STREP 2005-2007  </li></ul><ul><li>Purpose:   “The invest...
<ul><li>Social translucence design </li></ul>
Social translucence design <ul><li>Goal:  Design knowledge management system to emphasize the social aspect  </li></ul><ul...
Social translucence design <ul><li>1/2  – Displaying social interesting events + Behavior recommendations </li></ul>Interv...
Social translucence design <ul><li>1/2 – Displaying social interesting events + Behavior recommendations </li></ul><ul><li...
Social translucence design <ul><li>1/2  - Stream of social interesting events (aka Facebook) </li></ul><ul><li>Patterns ab...
Social translucence design 2/2  Indicators about the social activity related to the message Diffusion in the community / a...
Social translucence design <ul><li>2/2  Indicators about the social activity related to the message </li></ul><ul><li>Indi...
<ul><li>orientation of the collective attention </li></ul><ul><li>&  </li></ul><ul><li>Alignment of the user’s attention t...
Orientation Community  & user alignment <ul><li>Objective 1:  To understand  the orientation of the community’s attention ...
Orientation Community  & user alignment <ul><li>Objective 1:  To understand  the orientation of the community’s attention ...
Orientation Community  & user alignment <ul><li>Objective 1:  To understand  the orientation of the collective attention d...
<ul><li>Objective1:  Orientation of the community’s attention during [t1, t2]  </li></ul><ul><li>Output:  For each attenti...
 
Orientation Community &  user alignment <ul><li>Objective 2:  for each level (resource, concept, user), to  understand and...
At the resource level: Outputs for objective 2: For each items, displaying indicators about the user’s alignment + Suggest...
Community Orientation & user alignment TODO (work quickly done: 1 week..) Participation alignment:  “Do I have participate...
<ul><li>Attention  based  </li></ul><ul><li>recommendation engine </li></ul>
Attention based recommendation engine Problem :  Is there a way to recommend me  the most important messages ? 1) Avoiding...
Research problem Question:   In a  rich  information (and social) environment,  How do I choose  items  (message, blog pos...
Attention based recommendation engine <ul><li>Economical aspect of Attention:  « Attention economy » </li></ul><ul><li>Bef...
Attention based recommendation engine <ul><li>Psychological aspect of « Attention » </li></ul><ul><li>“ Visual” Attention ...
How does an item attract the user’s attention? <ul><li>Similarity of the problem in vision </li></ul><ul><li>In a scene  (...
How does an item attract the user’s attention? <ul><li>Attention guiding the 2 types of features: </li></ul><ul><li>Top-do...
How does an item attract the user’s attention? Process  1) For each attractive feature,  the signals are computed into a  ...
In your context of communication signals…  Question 1:  What are the top-down features  (user’s interest profile)  ?  Ques...
Question 1:  What are the top-down features?  (User driven attention) <ul><li>Top-down features </li></ul><ul><li>Message’...
Question 2:  What are attractive bottom-up features?  (i.e. without knowing the user’s  intention)
Attention based recommendation engine <ul><li>Conclusion: features of this ranking model </li></ul><ul><li>Based on a Visu...
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Nicolas Previous Works Meeting Turin Mai08

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  • Nicolas Previous Works Meeting Turin Mai08

    1. 1. Brief presentation of my previous work Nicolas Maisonneuve – Associate Researcher at Sony CSL Blog: http://nico.maisonneuve.free.fr Tagora Project – Mai 2008
    2. 2. My current & new Interest <ul><li>Human/Organization sciences + computer science </li></ul><ul><li>(e.g. the new interdisciplinary initiative: </li></ul><ul><li>MIT Colllective Intelligence Centre ) </li></ul>
    3. 3. My previous project: Atgentive <ul><li>European project: 2-year STREP 2005-2007 </li></ul><ul><li>Purpose: “The investigation about the use of artificial agents to support the management of the attention in an online community” </li></ul><ul><li>Experimentation: Private platform, small group ( ~20 persons) </li></ul><ul><li>(on going) personal research: </li></ul><ul><li>Social translucence design </li></ul><ul><li>Community orientation & user’s Alignment </li></ul><ul><li>Attention based recommendation engine </li></ul><ul><li>Simple attention analysis (Other’s attention to me vs. my attention to the others) </li></ul><ul><li>Little experience about attention spamming </li></ul><ul><li>Dynamic Feeds for granular problem of the activity </li></ul>
    4. 4. <ul><li>Social translucence design </li></ul>
    5. 5. Social translucence design <ul><li>Goal: Design knowledge management system to emphasize the social aspect </li></ul><ul><li>( Erickson et al. (2002). “Social translucence: designing social infrastructures that make collective activity visible ” ) </li></ul><ul><li>What emphasizing the social aspect important? </li></ul><ul><li>User’s perception change: From a repository of resources to a community of people </li></ul><ul><li>Emergence of social mechanisms(reputation, trust , affinity) </li></ul><ul><li>Increasing the participation (main problem in Knowledge Management) </li></ul>
    6. 6. Social translucence design <ul><li>1/2 – Displaying social interesting events + Behavior recommendations </li></ul>Interventions done by external agents that: Input: Observation the community’s activity D etect a given activity pattern: - Basic pattern: (e.g. the creation of a response) - More complex pattern: (a burst of activity) Output: a Feed/list of personalized recommendations for each user Objective: To help the user to better perceive/understand what happens in his/her community (c.f FaceBook MiniFeed but before them)
    7. 7. Social translucence design <ul><li>1/2 – Displaying social interesting events + Behavior recommendations </li></ul><ul><li>Patterns about the user’s state </li></ul><ul><li>New user: &quot;I can give you a presentation of the community platform, complete your profile“ + notifying the community about a new user </li></ul><ul><li>2) Inattentive user: Emailing inattentive user with an activity report to attract their attention </li></ul>Patterns about Activity related to your resources 3 ) “ UserA viewed your profil” or “UserB responded to your message “ 4) Burst of collective activity: “ Your profile/message was viewed by a high number of people (12 member(s)) [..]compared to the normal audience” 5) Special interest : “2 members UserA, UserB viewed your profile more frequently than the others”
    8. 8. Social translucence design <ul><li>1/2 - Stream of social interesting events (aka Facebook) </li></ul><ul><li>Patterns about resource’s lifecyle </li></ul><ul><li>6) Related to the message’s deadline </li></ul><ul><ul><li>For the inattentive members, few days before: “you haven't viewed The message XXX closed to its deadline, maybe you should read it” </li></ul></ul>Patterns about your behavior 7) Attraction power/charisma : “you’re loosing/gaining some audience compared to the last month” 8) Diversity of attention foci: “You don’t enough diversify your interest (focus on same people or same tags)?”
    9. 9. Social translucence design 2/2 Indicators about the social activity related to the message Diffusion in the community / audience of a resource - is my message well diffused in the community? - Is everybody aware about these posting? Lifecycle of a resource - “is the resource dead?” - “is there a burst of activity now or these last days?” Social aspect - who were the last readers? - who was interested by this document?
    10. 10. Social translucence design <ul><li>2/2 Indicators about the social activity related to the message </li></ul><ul><li>Indicators for 2 types of user’s activity </li></ul><ul><li>(2 levels of engagement): </li></ul><ul><ul><li>Awareness (access to a message) </li></ul></ul><ul><ul><li>Reaction (response to a message) </li></ul></ul><ul><li>These indicators could be used also to described the user’s activity or tag’s activity </li></ul>
    11. 11. <ul><li>orientation of the collective attention </li></ul><ul><li>& </li></ul><ul><li>Alignment of the user’s attention to the community’s one. </li></ul>
    12. 12. Orientation Community & user alignment <ul><li>Objective 1: To understand the orientation of the community’s attention during the interval [t1, t2] following 3 levels: </li></ul>Resource: which resources (message, user’s profile) got the most attention? Attention Space ( an attention focus = a resource)
    13. 13. Orientation Community & user alignment <ul><li>Objective 1: To understand the orientation of the community’s attention during the interval [t1, t2] following 3 levels: </li></ul>Resource: which resources (message, user’s profile) got the most attention? Concept: Which concepts (tag/keyword) got the most attention? Attention Space (an attention focus = a concept)
    14. 14. Orientation Community & user alignment <ul><li>Objective 1: To understand the orientation of the collective attention during the interval [t1, t2] following 3 levels: </li></ul>Resource: which resources (message, user’s profile) got the most attention? Concept: Which concepts (tag/keyword) got the most attention? User: which member got the most attention? Attention space (attention focus = a user )
    15. 15. <ul><li>Objective1: Orientation of the community’s attention during [t1, t2] </li></ul><ul><li>Output: For each attention space (focus = resource, tag, user) </li></ul><ul><li> A ranking of elements sorted by their weights in the orientation of the community’s attention. </li></ul><ul><li>Their weights in the collective attention = Attention metrics </li></ul><ul><li>Resource Space: Audience of the resource during [t1, t2] </li></ul><ul><li>Social/Tag Space: Sum of the audiences of the resources related to a specific user or / tag during [t1, t2] </li></ul>Orientation Community & user alignment
    16. 17. Orientation Community & user alignment <ul><li>Objective 2: for each level (resource, concept, user), to understand and help a user to regulate his attention by aligning it with the community’s one: </li></ul>Meta cognition level: I s my attention oriented to the same resources ( or same concepts, same users) as the community’s attention? Regulation: “What should I read, or who should I read to improve my alignment?”
    17. 18. At the resource level: Outputs for objective 2: For each items, displaying indicators about the user’s alignment + Suggestion to regulate the user’s behavior Orientation Community & user alignment Focus on me Inattention Same focus Only me At the tag /people level: What metrics do we want? “ The more the user is aware about popular resources (or active resources related to a popular tag), the more he is aligned with his community during [t1, t2]”
    18. 19. Community Orientation & user alignment TODO (work quickly done: 1 week..) Participation alignment: “Do I have participated the hot topics?” (Alignment user’s participation/collective participation) Temporal alignment: “do I have stable foci of attention (reading always resources related to the same users/tags?” (Alignment past activity / user Present) User’s Interest alignment: “do I have a dispersed behavior according to my declared interest ?” ( Alignment user’s attention/user’s intention) Add the notion of engagement: presence->reading-> participating “ which tag /discussion stimulated the most the community (i.e having generated the most resources related to it) during [t1, t2]?”
    19. 20. <ul><li>Attention based </li></ul><ul><li>recommendation engine </li></ul>
    20. 21. Attention based recommendation engine Problem : Is there a way to recommend me the most important messages ? 1) Avoiding uninteresting messages according my interests, 2) … except if it’s about an important issue in the community <ul><li>Situation </li></ul><ul><li>Member of an active community </li></ul><ul><li>I’m overwhelmed by the unread messages </li></ul><ul><li>I only have 10 minutes to understand the highlights since my last login. </li></ul>
    21. 22. Research problem Question: In a rich information (and social) environment, How do I choose items (message, blog posting, .. ) due to my limited resources (e.g. time, or people)? Answer: the notion of attention economy “ in a rich information environment, information competes for the user’s attention”  I choose the most attractive items (n ot only about the user’s interest or what expect the user)  Attention-based Ranking Model to select items
    22. 23. Attention based recommendation engine <ul><li>Economical aspect of Attention: « Attention economy » </li></ul><ul><li>Before: information has value because of its scarcity </li></ul><ul><li>Now: Overload of information=> information has no value. </li></ul><ul><li>What is still a limited resource is the people’s attention </li></ul><ul><li> Attention has value  New economy  information compete for the attention </li></ul><ul><li>(Davenport et al, 2001, “The Attention Economy: Understanding the New Currency of Business” Harvard Business School Press) </li></ul>
    23. 24. Attention based recommendation engine <ul><li>Psychological aspect of « Attention » </li></ul><ul><li>“ Visual” Attention guiding the 2 types of factors: </li></ul><ul><li>Top-down /voluntary factor ( User guidance ) </li></ul><ul><li>e.g. user searching a green object </li></ul><ul><li>B ottom-up / unvoluntary factors ( Stimuli guidance ) </li></ul><ul><li>e.g. flashy object in a dark scene </li></ul>
    24. 25. How does an item attract the user’s attention? <ul><li>Similarity of the problem in vision </li></ul><ul><li>In a scene (visual rich environment) , which area (item) will attract my attention? </li></ul><ul><li>how to predict where my attention will be guided? (Visual Search problem) </li></ul><ul><li>Approach </li></ul><ul><li>Use of a visual search model: “guided Search2.0” (J. Wolfe, 1994) </li></ul><ul><li>Turn visual signals into communication signals </li></ul><ul><li>(Message Reader = eye to perceive the social activity) </li></ul>
    25. 26. How does an item attract the user’s attention? <ul><li>Attention guiding the 2 types of features: </li></ul><ul><li>Top-down features ( User guidance ) </li></ul><ul><li>e.g. user searching a green object </li></ul><ul><li>B ottom-up features ( Stimuli guidance ) </li></ul><ul><li>e.g. flashy object in a dark scene </li></ul>Saliency (i.e. attractivity) of an item The saliency of a signal is computed as the (weighted) sum of the saliency for each attractive feature of the signal (e.g. color, size, intensity, motion,etc…) The Visual attention model “Guided Search 2.0” - 1/2
    26. 27. How does an item attract the user’s attention? Process 1) For each attractive feature, the signals are computed into a Feature Map (i.e. their levels of saliency according to the feature) 2) Mix of the feature Maps into a global Saliency Map The Visual attention model Guided Search 2.0 - 2/2
    27. 28. In your context of communication signals… Question 1: What are the top-down features (user’s interest profile) ? Question 2: What are the bottom-up features? (i.e. attractive features without knowing the user’s intention) Question 3: How to compute a feature map? Question 4: how to compute the saliency map?
    28. 29. Question 1: What are the top-down features? (User driven attention) <ul><li>Top-down features </li></ul><ul><li>Message’s Topic: focus on specific topics </li></ul><ul><li>Message’s User: focus on specific users </li></ul><ul><li>Simple Vigilance profile P </li></ul><ul><li>For a given context K (e.g. a task to do) , P(k) = (C,W) with: </li></ul><ul><ul><li>- C = The set of concepts c (user, topic) I want to pay specially attention to in a signal </li></ul></ul><ul><ul><li>W = their respective levels of vigilance w c for the user </li></ul></ul><ul><li>+ Limited capacity H ( ∑ w c <H and w c > w min ) ( I can’t want to pay attention to everything) </li></ul><ul><li> Vigilance feature map </li></ul>
    29. 30. Question 2: What are attractive bottom-up features? (i.e. without knowing the user’s intention)
    30. 31. Attention based recommendation engine <ul><li>Conclusion: features of this ranking model </li></ul><ul><li>Based on a Visual Attention Model </li></ul><ul><li> Not only what the user expects ( bottom up feature) </li></ul><ul><li>Use of social factors to rank items. </li></ul><ul><li>Try to integrate the notions of limited capacity & vigilance </li></ul><ul><li>Adaptive to the context (possible change of the vigilance profile) </li></ul>

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