From Zero to Cybersecurity Analyst

Balanced Neighborhoods for Multi-sided Fairness in Recommendation

Balanced Neighborhoods for Multi-sided Fairness in Recommendation

ACM FAccT Conference via YouTube Direct link

Conclusion

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10 of 10

Conclusion

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Balanced Neighborhoods for Multi-sided Fairness in Recommendation

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  1. 1 Introduction
  2. 2 What is different about this work
  3. 3 The filter bubble
  4. 4 Do we really need recommendation
  5. 5 Other considerations
  6. 6 Kiva example
  7. 7 Bias in data
  8. 8 Balanced neighborhoods
  9. 9 Sensitivity
  10. 10 Conclusion

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