1. Brief presentation of my previous work Nicolas Maisonneuve – Associate Researcher at Sony CSL Blog: http://nico.maisonneuve.free.fr Tagora Project – Mai 2008

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?

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]”

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]?”

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

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

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?

30. Question 2: What are attractive bottom-up features? (i.e. without knowing the user’s intention)