Aspects and nuance of recommendation algorithms.

1. +
Recommendation Assumptions
Prototyping Day @ Mozilla London
3 September 2015
Engineering Summit @ BBC
7 March 2018
Presented at

2. +
Recommendations
types
Personal vs. Non personal

3. +
Recommendations types
 Personalized
 User-interaction driven (passive/active)
 Non personalized
 Editorially curated
 Stats Based

4. +
User-interaction driven
passive interaction
active interaction
A set of user interactions with the
system entails a set of (possibly
empty) preferences - inference
Can be used to reinforce
recommendations (like, thumbup,
follow, dislike, etc.)

5. +
Recommendations types
 Personalized
 User-interaction driven (passive/active)
 Non personalized
 Editorially curated
 Stats Based

6. +
Editorially curated
content
The BBC

7. +
User rating
Google

8. +
Most popular
If more than N% + K of people liked,
listened to, blablablabed about
something (+/- standard deviation), this
something is likely to be
recommended.

9. +
Content similarity
It can be content-to-content or cross content. To calculate the similarity we
can use the actual content or the metadata or both.

10. +
Co-occurrence
relationship (market
basket analysis)
Understand what products or services
are commonly purchased together.
If you consume a certain group
(cluster) of contents, you are more (or
less) likely to consume another group
of items – Beers and nappies

11. +
Assumptions
 Similarity and Correlation
 User activity assumptions
 Device sensor analytics (accelerometer, gyroscope, compass,
barometer, ambient light sensor, proximity sensor,
thermometer, camera, microphone, GPS, etc.)

12. +
Perturbations (external influences)
 Model building phase
 Recommendation phase

13. +
Mood

14. +
Wear & Tear

15. +
Does the time matter?

16. +
Does the time matter?
 Are preferences or statistics about a topic valid forever?
 If not, what’s the “best” time window to take in account?
 How can we model a preference modification during the time?
(Aging, change of taste)
 How a good/bad feedback can affect a recommendation in the
future and for how long?

17. +
Empathy (induced preferences)

18. +
I’ve got questions for you
 Inheritance, taxonomy, membership, how do we use these
relationships between contents in a domain in order to infer a
user interest?
 Is the interest of a user black and white only? How can I
express different type of interests (if any)?
 Can we improve the quality of the user experience by
presenting the right “amount” of information of a recommended
content according to
 type and level of interest?
 time available to consume the content?
 mood?

19. +
Thank you
Simone.Spaccarotella@bbc.co.uk
Senior Software Engineer | BBC Sounds