Nicolas Maisonneuve, research associate Centre for Advanced Learning Technologies, INSEAD Application of a simple visual attention model to the communication overload problem Tags: Information overload, Community, Social Media, Attention‐ based Ranking model, visual attention model, Social computing Context: European research project www.atgentive.com Sept. 2007
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Nicolas Maisonneuve, research associate Centre for Advanced Learning Technologies, INSEAD
Application of a simple visual attention model to the communication overload problem
Tags: Information overload, Community, Social Media, Attention‐based Ranking model, visual attention model, Social computing
Context: European research project www.atgentive.com
Sept. 2007
Scenario 1: Online Community
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
Situation• Member of an active community• I’m overwhelmed by the unread messages• I only have 10 minutes to understand the highlights since my last login.
Scenario 2: Weblogs & Social Media
Situation
• I have subscribed to a lot of interesting blogs
• Now I’ m overloaded by too many posts
• I only have 10 minutes to read all my feeds
Same Question:
How rank them and read only the most important ones for me ?
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: in a rich information environment, information competes for the user’s attention (c.f Attention Economy)
I choose the most attractive items
Conception of an Attention‐based Ranking Model to select items
How does an item attract the user’s attention?
Similarity in vision• In a scene (visual rich environment), which area (item) will attract my attention? • how to predict where my attention will be guided? (Visual Search problem)
Approach • Use of a visual search model: “guided Search2.0” (J. Wolfe, 1994)• Turn visual signals into communication signals (Message Reader = eye to perceive the social activity)
How does an item attract the user’s attention?
Attention guiding the 2 types of features:• Top‐down features (User guidance)e.g. user searching a green object
• Bottom‐up features (Stimuli guidance) e.g. flashy object in a dark scene
Saliency (i.e. attractivity) of a signalThe 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
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
How does an item attract the user’s attention?
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?
Question 1: What are the top‐down features? (User driven attention)
Top‐down features•Message’s Topic: focus on specific topics
•Message’s User: focus on specific users
VG Market IT Industry Research
User's vigilance profile in a IT Community (scenario 1)
userA userB userC
User's vigilance profile in a Social Network (Scenario3)
Simple Vigilance profile P For a given context K (e.g. a task to do) ,
P(k) = (C,W) with:‐ C = The set of concepts c (user, topic) I want to pay specially attention to in a signal‐W = their respective levels of vigilance wcfor the user
‐ + Limited capacity H ( ∑wc<H and wc>wmin )(I can’t want to pay attention to everything)
Vigilance feature map
Question 2: What are attractive bottom‐up features? (i.e. without knowing the user’s intention)
1) Exception (temporal/spatial)
‐ Unusual sender
‐ Unusual topic
‐ Unusual activity (cf 5)
2) About me
‐message audience focussed on me (mailing‐list vs. personal message)
3) User’s effort
‐ Type of Medium
(Text < Sound< Video)
4) Urgency
Lifecycle of the message (3 months<now)
‐ See also 5)
5) Other’s influence
‐ Collective attention (burst of activity)
‐ Explicit Attention asked (Subject: [URGENT]… )
Question 3: How to compute a feature map?
Computation of a bottom‐up feature mapE = the set of unread items e1, e2, .. , en • For each feature k , each item is computed by a function fk to give its saliency [0, 1] related to this feature•A feature map is Mk={fk (e1), fk (e2), .. , fk (en)}
Example: Simple Computation of the Burst of (reading) Activity featureDefinition: Burst = an abnormal high level of activity : Last week, in average, a message has been read 10 times, but the message A has been read 30 times.
Computation:r(e,∆t) = the number of readings of the message e during the interval ∆t, m = the mean of r(e, ∆t) for the set of messages read during ∆tfburst(e)= with 1<t1<t2 the bounds
• Need to be evaluated (how to configure the weight of each Feature in the global saliency map computation?)
Features of the Ranking Model• Based on a Visual Attention Model
Not only what the user expects ( bottom up feature)• Use of social factors to rank items. • Try to integrate the notions of limited capacity & vigilance•Adaptive to the context (possible change of the vigilance profile)
Thanks for your attention. ☺
Scenario 3: Traditional Communication
Problem
Is there a way to notify me on a new emails only if :
‐ it is related to my current task (e.g. message from UserB)
‐ Or it delivers unexpected but important information.
Situation :
• Growth of the user’s connectivity (globalization + internet)
• I’m currently collaborating on a specific task with userA and specially with userB.
• I receive a lot of emails that interrupt my work
• 4 hours spent managing emails per day by senior management (Guardian Unlimited Newspaper, 2007)
• Economic Impact of the interruption caused by email+online tools: $588 billion/year for the Us Economy (Basex Research, 2005)