The document discusses the impact of algorithms on attention and information consumption, highlighting that personalized recommendations can significantly enhance user engagement and drive revenue. It outlines essential ingredients for effective recommender systems, including user metadata and behavioral insights, while also noting the importance of balancing exploitation and exploration in recommendation strategies. Data shows an improvement in user score over time, reflecting the effectiveness of machine learning in optimizing platforms based on user activity.

1. Recommended for you Related Recent Liked
Recommendations you can trust

2. “ ”
THE WAR FOR ATTENTION
Information consumes the
attention of its recipients.
Hence a wealth of
information creates a
poverty of attention.
Herbert Simon, 1971

3. +10,000,000,000
hours a week

4. IT EVEN COMPETES WITH SLEEP

5. Algorithms
decide for us

6. +80%
of time spent comes from
algorithmic recommendations

7. We think the combined effects of
personalisation and recommendations
earn us more than
$1bn per year
“ ”
RECOMMENDATION SHOWS ON
THE BOTTOM LINE

8. WHAT IS A GOOD
RECOMMENDER
SYSTEM FOR
LEARNING?

9. RELATED COURSES
FOR BETTER
DISCOVERABILITY

10. CONTENT BEHAVIOUR
USER
METADATA
INSIGHTS
TOPIC MODELLING
SESSION PATTERNS
LEARNING PATHWAYS
CONTENT POPULARITY
GOALS
DEMOGRAPHICS
TRIBES
INTERESTS
THE INGREDIENTS OF GOOD
RECOMMENDATION

11. 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

12. MORE USERS
SMARTER
ALGORITHMS
MORE DATABETTER PRODUCT
MACHINE LEARNING OPTIMISES YOUR
PLATFORM BASED ON USER ACTIVITY

13. 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

14. PUSH
CONTENT
GET GOINGCONTENT IS
UNDERSTOOD
PICK A MODULE
RECOMMENDATION
AS A SERVICE

15. RECOMMENDED
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