gregab
December 13, 2013
190

# Order Matters: Accounting for Anchoring Bias on User Labeling in Recommendation Systems

Read the full paper here: http://gregborenstein.com/assets/order-matters.pdf

Final project for an Interactive Machine Learning course at the MIT Media Lab. Demonstrates the presence of Anchoring Bias, as described by Kahneman and Tversky, in sequential ratings interaction common to recommender systems like Netflix.

## gregab

December 13, 2013

## Transcript

1. ### Order Matters: Accounting for Anchoring Bias on User Labeling in

Recommendation Systems Greg Borenstein MAS.S62: Interactive Machine Learning
2. ### When “people assess the probability of an uncertain event or

the value of an uncertain quantity” they “rely on a limited number of heuristic principles” that “sometimes lead to severe and systematic errors” Kahneman and Tversky Judgement Under Uncertainty: Heuristics and Biases, 1974 Cognitive Bias
3. ### When “people assess the probability of an uncertain event or

the value of an uncertain quantity” they “rely on a limited number of heuristic principles” that “sometimes lead to severe and systematic errors” Kahneman and Tversky Judgement Under Uncertainty: Heuristics and Biases, 1974 Cognitive Bias
4. ### initial value + insufﬁcient adjustment bias towards the initial values

Kahneman and Tversky Judgement Under Uncertainty: Heuristics and Biases, 1974 Anchoring Bias
5. ### Percentage of African Countries in the UN? Image courtesy of

Wikimedia Foundation.

1 2 1 2

18. ### P(R=i ) * (r-i) i = 1 5 2 Σ

argmin r Order-Ignorant Constant prediction that minimizes mean squared error

20. ### Order-Aware Adjusted(R ) = Original(R ) + k*W(R ) n

n Treat previous ratings as labels n-1
21. ### Order-Aware Treat previous ratings as labels Adjusted(R ) = Original(R

) + k*W(R , dt?) n-1 n n
22. ### r - r Order-Aware Treat previous ratings as labels W(r

) = 1/n * i-1 i = 1 n Σ i i

n-1

31. ### Next steps integrate MovieLens recommendations Adjusted(R ) = Original(R )

+ k*W(R , dt?) n-1 n n