in other news

In Other NewsBroaden your horizons by reading like a writerCharlotte Greenan

Using a social network of journalists to recommend news articles from sections you wouldn’t click on.

How can we find articles you might like from sections that you wouldn’t normally look at?

How can we find articles you might like from sections that you wouldn’t normally look at?

? ?

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InOtherNews.xyz

DataTwitter API

47,000 network relations

Twitter and Guardian APIs

1,300 Guardian journalists

Guardian API

300 articles daily

(10,000+ total)

Transitivity

USER

JOURNALIST 1

JOURNALIST 2

likes to read

likes to read

might like to read?

How can transitivity help to solve our problem?

USERwho likes

sport

SPORT

SPORT

SPORT

Narrow horizons!

How can transitivity help to solve our problem?

USERwho likes

sport

SPORT

World

SPORT

SPORT

Music

TV

Politics

Business

SportBroad horizons!

User-based recommender algorithm

Initial recommendations Weighted k-nearest

neighbors

User input

User feedback

Updated recommendationsIncorporating upvotes as

additional weighted neighbors.

Journalist featuresNeighbourhood component

analysis

User-based recommender algorithmLeave-one-out cross validation

Initial recommendations Weighted k-nearest

neighbors

User input

51% more correct followees (than just

recommending most popular journalists).

Up to 59% more correct followees.

User feedback

Updated recommendationsIncorporating upvotes as

additional weighted neighbors.

Journalist featuresNeighbourhood component

analysis

Charlotte Greenan

Homophily

Transitivity

12 times as many

triangles as a random graph

Data◎ Articles from Guardian API.◎ Social network from Twitter

API.

Leave-one-out cross validationImprovement in correctly predicted ties by section

Neighbourhood component analysisTransforming similarities between sections

Before After

Neighbourhood component analysisTransforming similarities between sections

Linear transformation of vectors indicating number of articles per section. Choose linear transformation :

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).

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)