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Estimating the causal impact of recommendation systemsAMIT SHARMA, JAKE HOFMAN, DUNCAN WATTSMICROSOFT RESEARCH, NEW YORK
2nd International Conference on Computational Social Science
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How much do they change user behavior?
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Naively, up to 30% of traffic comes from recommendations
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Naively, up to 30% of traffic comes from recommendations
“Burton Snowboard, a sports retailer, reported that personalized product recommendations have driven nearly 25% of total sales since it began offering them in 2008. Prior to this, Burton’s customer recommendations consisted of items from its list of top-selling products.”
Almost surely an over-estimate of the actual effect, because of correlated demand between a product and its recommendations.
Example: product browsing on Amazon.com
Example: product browsing on Amazon.com
Example: product browsing on Amazon.com
Counterfactual browsing: no recommendations
Counterfactual browsing: no recommendations
Problem: Correlated demand may drive page visits, even without recommendations
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The problem of correlated demand
Demand for winter
accessories
Visits to winter hat
Rec. visits to winter
gloves
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Goal: Estimate the extra activity caused by a recommender system that would not have happened otherwise
Causal
Convenience
OBSERVED CLICK-THROUGHS WITHOUT RECOMMENDER
Convenience
?
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Ideal experiment: A/B Test
Treatment (A)Control (B)
But, experiments:may be costlyhamper user experiencerequire full access to the system
Experiments may be costly or infeasible.
Can we derive an observational method to identify the causal effect of recommendations?
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Using natural variations to simulate an experiment
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Studying sudden spikes, “shocks” to demand for a book
[Carmi et al. 2012]
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The same author’s recommended book may also have a shock
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Past work: Controlling for correlated demand
Uses statistical models to control for confounds Carmi et al. [2012], Oestreicher and Sundararajan [2012] and Lin [2013] construct “complementary sets” of similar, non-recommended products.
Garfinkel et. al. [2006] and Broder et al. [2015] compare to model-predicted clicks without recommendations.
But, 1. These assumptions are hard to verify.2. Finding examples of valid shocks requires ingenuity
and restricts researchers to very specific categories
Shock-IV: A simpler, more robust method for estimating causal impact.
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Distinguishing between recommendation and direct traffic
All visits to a product
Recommender visits Direct visits
Search visits
Direct browsing
Proxy for unobserved demand
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The Shock-IV strategy: Searching for valid shocks
? ?
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The Shock-IV strategy: Filtering out invalid shocks
Search for products that receive a sudden shock in their traffic but direct traffic for their recommendations remains constant.
Why does it work? Shock as an instrumental variable
Demand
Focal visits (X)
Rec. visits (Y)
Sudden Shock
Directvisits (Y)
Computing the causal estimate
Increase in recommendation clicks (Δr)
Causal CTR (ρ) = Δr/Δv
*Same as Wald estimator for instrumental variables
Increase in visits to focal product (Δv)
The shock-IV strategy: In equations
At any time t:
When product i experiences a shock in page visits:
(Because constant dj implies constant convenience visits)
Application to Amazon.com, using Bing toolbar logs
Anonymized browsing logs:
• 23 million pageviews
• 1.3 million Amazon products
• 2 million Bing Toolbar users
Sept 2013-May 2014
Recreating sequence of page visits by a user
Search page Focal product page Recommended product page
Recreating sequence of page visits by a user
Timestamp URL2014-01-20 09:04:10
http://www.amazon.com/s/ref=nb_sb_noss_1?field-keywords=George%20saunders
2014-01-20 09:04:15
http://www.amazon.com/dp/0812984250/ref=sr_1_1
2014-01-20 09:05:01
http://www.amazon.com/dp/1573225797/ref=pd_sim_b_2
Recreating sequence of page visits by a user
Timestamp URL2014-01-20 09:04:10
http://www.amazon.com/s/ref=nb_sb_noss_1?field-keywords=George%20saunders
2014-01-20 09:04:15
http://www.amazon.com/dp/0812984250/ref=sr_1_1
2014-01-20 09:05:01
http://www.amazon.com/dp/1573225797/ref=pd_sim_b_2
User searches for George Saunders
User clicks on the first search result
User clicks on the second recommendation
I. Weekly and seasonal patterns in traffic, nearly tripling in holidays
II. 30% of all pageviews come through recommendations
III. Books and eBooks are the most popular categories by far
IV. Apparel and shoes see a substantially higher fraction of visits through recommendations
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Shock-IV: Finding shocks in user visit data
We look for focal products with large and sudden increases in views relative to typical traffic.
Size of shock exceeds:◦ 5 times median traffic◦ Shock exceeds 5 times the previous day's traffic and 5 times the
mean of the last 7 days.
Shocked product has: ◦ Visits from at least 10 unique users during the shock◦ Non-zero visits for at least five out of seven days before and after
the shock
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Shock-IV: Ensuring exclusion restriction
Recommended product (Y) should have constant direct visits during the time of the shock.
(1-β): Ratio of maximum 14-day variation in visits to a recommended product to the size of the shock for the focal product.
Direct traffic to Y is stable relative to the shock to the focal product.
β = 1 Direct traffic to Y is no less varying than the shock to focal product.
β = 0
How to choose
Focal product visits Rec. product direct visits
Focal product visits Rec. product direct visits
Accept
RejectSelect
Using the method, obtain >4000 natural experiments!
20% of all products that had visits on any single day.
Estimating the causal clickthrough rate ()
ρ =Δrxyt*/ Δvxt*
At β = 0.7, causal CTR =3%.
Causal click-through rate by product category
What fraction of the observed click-throughs are causal?
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Estimating fraction of observed click-throughs that are causal
Compare the number of estimated causal clicks to all observed recommendation clicks (non-shock period).
λ = ρxy.vxt / rxyt
Only a quarter of the observed click-throughs are causal
At β = 0.7, only 25% of recommendation traffic is caused by the recommender.
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Generalization? Shocks may be due to discounts or sales
Lower CTR may be due to the holiday season
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Local average treatment effect (LATE), not fully generalizable
Shocked products are not a representative sample of all products, nor are the users who participate in them.
• Shock-IV method covers roughly one-fifth of all products with at least 10 visits on any single day.
• Our results are robust to sale or holiday effects. • Causal estimates are consistent with
experimental findings (e.g., Belluf et. al. [2012])
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More generally…A robust, scalable method for causal inference.
◦ Causal CTR for Amazon’s recommender system much less than the naïve observational CTR.
◦ Can be applied to other domains, such as online ads.
Data mining for instruments I. Allows us to study a much larger sample of natural experiments, while being able to test for exclusion restriction directly.
II. Can be used for finding potential instruments.
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Thank you!AMIT SHARMA
MICROSOFT RESEARCH http://www.amitsharma.in
Sharma, A., Hofman, J. M., & Watts, D. J. (2015). Estimating the causal impact of recommendation systems from observational data. In Proceedings of the Sixteenth ACM Conference on Economics and Computation.
Shock-IV: A robust, scalable method for estimating causal impact from observational data, with testable assumptions.
Naïve observational estimates of CTR for recommendation systems may be big overestimates.