1. In Other News
Broaden your horizons by
reading like a writer
Charlotte Greenan
Using a social network of
journalists to recommend
news articles from sections
you wouldn’t click on.
2. How can we find articles you might
like from sections that you wouldn’t
normally look at?
3. How can we find articles you might
like from sections that you wouldn’t
normally look at?
? ?
7. How can transitivity help to solve our
problem?
USER
who likes
sport
SPORT
SPORT
SPORT
Narrow
horizons!
8. How can transitivity help to solve our
problem?
USER
who likes
sport
SPORT
World
SPORT
SPORT
Music
TV
Politics
Business
SportBroad
horizons!
9. User-based recommender algorithm
Initial recommendations
Weighted k-nearest
neighbors
User
input
User
feedback
Updated recommendations
Incorporating upvotes as
additional weighted
neighbors.
Journalist features
Neighbourhood component
analysis
10. User-based recommender algorithm
Leave-one-out cross validation
Initial recommendations
Weighted k-nearest
neighbors
User
input
Optimizing k gives 51%
more correct followees
than just recommending
most popular journalists.
Optimizing weights
gives up to 5% more
correct followees than
using method above
alone.
User
feedback
Updated recommendations
Incorporating upvotes as
additional weighted
neighbors.
Journalist features
Neighbourhood component
analysis
20. Recommendation algorithm
◎ k most similar journalists, (cosine similarity);
◎ Journalists you like, (user feedback);
◎ Journalists you don’t like, (user feedback).
◎ Order journalists by their score:
◎ Recommend journalists using order until all
sections recommended (or score is zero).
21. Credits
Special thanks to all the people who made and released
these awesome resources for free:
◎ Simple line icons by Mirko Monti
◎ E-commerce icons by Virgil Pana
◎ Streamline iconset by Webalys
◎ Presentation template by SlidesCarnival
◎ Photographs by Unsplash & Death to the Stock Photo
(license)
