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

Sensitivity

9 of 10
Previous
Next

9 of 10

Sensitivity

Class Central Classrooms beta

YouTube videos curated by Class Central.

Classroom Contents

Balanced Neighborhoods for Multi-sided Fairness in Recommendation

Automatically move to the next video in the Classroom when playback concludes
  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

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Sign up for free
Someone learning on their laptop while sitting on the floor.