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“ ”
THE WAR FOR ATTENTION
Information consumes the
attention of its recipients.
Hence a wealth of
information creates a
poverty of attention.
Herbert Simon, 1971
+10,000,000,000
hours a week
IT EVEN COMPETES WITH SLEEP
Algorithms
decide for us
+80%
of time spent comes from
algorithmic recommendations
We think the combined effects of
personalisation and recommendations
earn us more than
$1bn per year
“ ”
RECOMMENDATION SHOWS ON
THE BOTTOM LINE
WHAT IS A GOOD
RECOMMENDER
SYSTEM FOR
LEARNING?
RELATED COURSES
FOR BETTER
DISCOVERABILITY
CONTENT BEHAVIOUR
USER
METADATA
INSIGHTS
TOPIC MODELLING
SESSION PATTERNS
LEARNING PATHWAYS
CONTENT POPULARITY
GOALS
DEMOGRAPHICS
TRIBES
INTERESTS
THE INGREDIENTS OF GOOD
RECOMMENDATION
REINVENTING RECOMMENDATIONS
Approach EXPLOITATION EXPLORATION
Ranking Popularity Trust
Focus Previous actions Future actions
User interface Black Box Transparent
User experience Fixed Flexible
Feedback Behaviors Values
Goal Engagement Outcomes
MORE USERS
SMARTER
ALGORITHMS
MORE DATABETTER PRODUCT
MACHINE LEARNING OPTIMISES YOUR
PLATFORM BASED ON USER ACTIVITY
HOW THE QUALITY HAS DEVELOPED
USER SCORE
(MAX 100%)
73.2%
60.4%
52.2%
APRIL 2017NOVEMBER 2016APRIL 2016
Qualitative survey on a control set of 2500 items
PUSH
CONTENT
GET GOINGCONTENT IS
UNDERSTOOD
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Bibblio - ASU GSV 2017

Notas do Editor

  1. MACHINE LEARNING IMPROVES THE SYSTEM BASED ON USER ACTIVITY Optimisation for both your platform and individual users.