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

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December 13, 2013

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.

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gregab

December 13, 2013
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Transcript

  1. 1.

    Order Matters: Accounting for Anchoring Bias on User Labeling in

    Recommendation Systems Greg Borenstein MAS.S62: Interactive Machine Learning
  2. 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. 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. 4.

    initial value + insufficient adjustment bias towards the initial values

    Kahneman and Tversky Judgement Under Uncertainty: Heuristics and Biases, 1974 Anchoring Bias
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    P(R=i ) * (r-i) i = 1 5 2 Σ

    argmin r Order-Ignorant Constant prediction that minimizes mean squared error
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    Order-Aware Adjusted(R ) = Original(R ) + k*W(R ) n

    n Treat previous ratings as labels n-1
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