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Algorithms for Position Bias Correction in Ranking Anirban Majumder Machine Learning, Amazon
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User Created Content @Amazon
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Content Ranking Rank content to improve customer experience Ranking Algorithms Rank Content User feedback Clicks, purchase …
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Content Ranking #Impressions = 1000 #clicks = 20 CTR = 0.02 #Impressions = 1000 #clicks = 10 CTR = 0.01 #Impressions = 1000 #clicks = 1 CTR = 0.001
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Content Ranking #Impressions = 1000 #clicks = 12 CTR = 0.012 #Impressions = 1000 #clicks = 10 CTR = 0.010 #Impressions = 1000 #clicks = 5 CTR = 0.005 What happens if we change the ordering ?
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Presentation Bias Eye-tracking experiment 1 on search result listing 1 “Google Eye Tracking Report”, by Enquiro, Eyetools, Did-It, 2005 ImpressionAction View Implication Self-fulfilling prophecy !
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Examination Model 2 Sample a position p Examine the item k at the position Take an action (purchase/no-purchase, click/no-click) Item Factor Position Factor
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Beta-Poisson Model A k,p : #actions (of item k from position p) I k,p : #impressions
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Beta-Poisson : Inference Exact posterior computation is difficult – interaction between α and β Variational Bayes approximation
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Data Sparsity Position data is often sparse Items appear in few positions Not possible to estimate the bias Use feature-based representation of Items Product category information Item price Review rating, review text
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Leveraging Side Information Items with similar feature vectors can share position data Relevance Factor Position Factor
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Experiments Experiments were performed on Amazon deals data. Data set DealsImpressionsSessionsPositions 9.3k315M11M350
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Offline Experiments : Predicting Purchase Rate Baselines – Click-over-Expected-Click (COEC 6 ) – Gamma-Poisson 7 6 “Comparing Click Logs and Editorial Labels for Training Query Rewriting”, Zhang et al, WWW’07 7 “Position-Normalized Click Prediction in Search Advertising”, by Chen et al, KDD ‘12
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Predicting Purchase Rate : Results Gamma- Poisson Beta-PoissonBeta-Poisson + features RMSE12.5x1.2x1.0 KL11.0x1.1x1.0 Accuracy of different bias correction models in predicting purchase rate at position 1 as measured by RMSE and KL divergence. MRRP@2P@5 COEC 0.85x0.94x0.96x Gamma-Poisson 0.91x0.94x0.95x Beta-Poisson 0.94x0.95x0.96x Beta-Poisson + features 1.0 The predicted purchase rate is used to rank deals. The performance is measured in terms of Mean Reciprocal Rank and precision@k
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Conclusion Position bias correction is important for many ranking problems. Position data can be sparse and will affect bias correction – use a feature based representation.
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Backup
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Methodology Held- out set Training Data number of Impressions, purchases Position 1
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