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Measuring Diversity in algorithmic recommendation sets

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

Published in: Data & Analytics
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Measuring Diversity in algorithmic recommendation sets

  1. 1. Measuring diversity Judith Möller, Damian Trilling, Natali Helberger, Bram van Es University of Amsterdam
  2. 2. Does algorithmic recommendation lead to less diversity? 2
  3. 3. Research question Which news recommender system produces the largest and smallest amount of pluralism on the different dimensions of diversity? 3
  4. 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. 5
  6. 6. Measuring diversity? Possible: Entropy in features (topic, named entities….) Much better: Distance of features in a space
  7. 7. 7
  8. 8. 8 Sport Domestic politics International Relations
  9. 9. 9 Sport Domestic politics International Relations d
  10. 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. 11. Work in progress…. Feature space
  12. 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. 13. Results
  14. 14. Thanks Want to know more? j.moller@uva.nl; @judith_moeller Personalised-communication.net

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