Presentation held on May 26 2017 at the ICA conference in San Diego

1. Measuring diversity
Judith Möller, Damian Trilling, Natali Helberger, Bram van Es
University of Amsterdam

2. Does algorithmic recommendation lead to
less diversity?
2

3. Research question
Which news recommender system produces the largest and smallest
amount of pluralism on the different dimensions of diversity?
3

4. Methods
Data: 1000 Simulated recommendation sets in different algorithmic
settings based of data on Volkskrant.nl published between 19.9.2016
and 26.9 2016, N=21,973 articles
• Benchmark: Recommendation by the human editor
4

5. 5

6. Measuring diversity?
Possible: Entropy in features (topic, named entities….)
Much better: Distance of features in a
space

7. 7

8. 8
Sport
Domestic
politics
International
Relations

9. 9
Sport
Domestic
politics
International
Relations d

10. Computational approach
• Feature distance matrix: Compute Euclidian distances between the
occurring feature values (topics, polarity, subjectivity, etc.)
• Document matrices: Apply document weights to the feature values
• Document distance matrix: Collect the Frobenius norms of the
differences between the document matrices
• Apply document distance matrix to recommendations

11. Work in progress….
Feature space

12. Next steps
Method assumes spaces of topics
 Apply to more complex and diverse data sets
 Future work needs to also include feedback loops
Development of DART (Diversity Assessment and Recommendation
toolkit)

13. Results

14. Thanks
Want to know more?
j.moller@uva.nl;
@judith_moeller
Personalised-communication.net