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.)
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
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?
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?