Déjà Vu: The Importance of Time and Causality in Recommender Systems
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This document discusses the importance of time and causality in recommender systems. It summarizes that (1) time and causality are critical aspects that must be considered in data collection, experiment design, algorithms, and system design. (2) Recommender systems operate within a feedback loop where the recommendations influence future user behavior and data, so effects like reinforcement of biases can occur. (3) Both offline and online experimentation are needed to properly evaluate systems and generalization over time.
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Déjà Vu: The Importance of Time and Causality in Recommender Systems
- 1. Déjà Vu The Importance of Time and Causality in Recommender Systems Justin Basilico & Yves Raimond August 29, 2017 @JustinBasilico @moustaki
- 3. But first… Goodbye & Cinematch % Match Hello &
- 4. But first… Goodbye Why? +200% ratings volume Clear link to personalization & Cinematch % Match Hello &
- 5. Image from Domiriel (cc by-nc)
- 6. ● This moment can be controlled by the user ○ Visit time ○ Session length ● … or influenced by the system ○ Notifications, emails ● And must choose an action ○ … that has consequences Recommendations are actions at a moment in time ● Time and causality are critical aspects in any recommender system ○ Data collection ○ Experiment design (offline & online) ○ Algorithm & objective design ○ System design
- 9. Violation of the space-time continuum! Observed labels Training input data collected Training time Serving time Serving input data collected
- 10. Data collection Observed labels Training input data collected Training time Serving time Serving input data collected
- 11. Time machines Observed labels Training input data collected Training time Serving time Serving input data collected Distributed Time Travel for Feature Generation
- 12. ● Be careful when splitting dataset ○ Don’t overfit the past ○ Predict the future ● Rule of thumb: Split across what you need to generalize ○ Time! ○ Users or Items? ● May need to train/test at multiple distinct time points to see generalization across time (e.g. [Lathia et. al., 2009]) ● Simulate system behaviors (e.g. training and publishing delays) in evaluation pipeline ○ Helps capture trade-off between accuracy and responsiveness Experiment design Train Time Test
- 14. R ≈ UM
- 15. ? ? Users changing over time Nonstationarity
- 16. Items changing over time popularity time Learned item bias Actual item popularity Item launch Item becomes available
- 17. ● Aggregation ○ Decay functions (e.g. [Ding, Li, 2005]) ○ Buckets (e.g. [Zimdars, Chickering, Meek, 2001]) ● Extrapolation (e.g. [Koren, 2009]) ● Sequences ○ Markov (e.g. [Rendle, et al., 2010]) ○ Last N (e.g. [Shani, Heckerman, Brafman, 2005]) ○ RNNs (e.g. [Hidasi et al., 2015]) ● Features ○ Discretized (e.g. [Baltrunas, Amatriain, 2009]) ○ Continuous (e.g. example age in [Covington et. al., 2016]) Some modeling approaches time
- 18. ● Generalizing to future behaviors through temporal extrapolation ● Time exhibits many periodicities ○ Daily ○ Weekly ○ Seasonally ○ … and even longer: Olympics, elections, etc. ● Additional periodic time context features can be added or extracted Time as context Experiment on a Netflix internal dataset
- 19. ● Recommendation systems are a means to an end ○ Reward = enjoyment - interaction cost ○ Enjoyment integrated over time (e.g. goodness * length of view) ○ Interaction cost integrated over time ○ Don’t waste your users time ○ Magnitudes of enjoyment and cost may be user-specific ● Maximize enjoyment of the selected item while minimizing time it takes to find the item Minimizing interaction time
- 20. Hangul alphabet, 3 syllables but requires 7 (2 + 3 + 2) interactionsClick
- 21. With a model optimized to minimize interaction time: one interaction Click
- 25. Algorithm C Algorithm B Algorithm A Algorithms changing Idea Offline experimentation Online experimentation (A/B) Rollout
- 26. Algorithm C Algorithm B Algorithm A Algorithms changing Idea Offline experimentation Online experimentation (A/B) Rollout Assumes stationarity! A change in other parts of the system might invalidate previous (offline or online) results. Holdback A/B tests as part of rollout can help.
- 27. UX changing over time % Match&
- 28. Feedback loops Impression bias inflates plays Leads to inflated item popularity More plays More impressions Oscillations in distribution of genre recommendations Feedback loops can cause biases to be reinforced by the recommendation system!
- 32. Closed Loop Training Data Watches Model Recs Danger Zone Search Training Data Watches Model Recs Open Loop
- 33. Closed Loop Training Data Watches Model Recs Danger Zone Search Training Data Watches Model Recs Open Loop
- 34. Open vs. Closed Loops [Based on Steck, 2013 with system as selector] Watch when rec Probability of rec Watch when not rec Probability of not rec
- 35. Open vs. Closed Loops [Based on Steck, 2013 with system as selector] Watch when rec Probability of rec Watch when not rec Probability of not rec Closed loop: 0 Open loop: > 0
- 36. Open vs. Closed Loops [Based on Steck, 2013 with system as selector] Watch when rec Probability of rec Watch when not rec Probability of not rec Closed loop: 0 Open loop: > 0 We have control over this
- 37. ● Maintain some controlled exploration to break feedback loop and handle non-stationarities ● Explore with -greedy, Thompson Sampling, etc. ● Control to avoid significantly degrading user experience ● Log as much as possible ○ Include counterfactuals: What maximal action system wanted to do (e.g. [Bottou et al., 2013]) Controlled stochasticity Explore Explore
- 38. Replay Metrics Observed reward Existing recommendation algorithm (with stochasticity) Observed reward New recommendation algorithm [Li et al., 2011; Dudik, Langford, Li, 2014] Simulate online metrics, offline!
- 39. ● Stochasticity opens the door to using causal inference ● Inverse Propensity Weighting ○ Reduce production bias by reweighting train and test data ○ Know probability of user receiving an impression ○ Doesn’t handle simultaneity and other endogeneity ● Covariate shift ○ Use explore data to estimate bias in other data ○ Use all data to train ● Instrumental variables for more general settings Causality [Schnabel et al., 2016; Liang, Charlin, Blei, 2016; Smola, 2011, Sugiyama, Kawanabe, 2012]
- 40. ● Most recommendations (and ML) models are correlational ○ These items are correlated with these types of users ● But we seek causal actions ○ Showing this item is rewarding for this user ● Our recommendation action should have an incremental effect in reward: E[r(a)] - E[r(∅)] ○ Application-dependent choice of ∅ ○ Sometimes it may be better not to provide a recommendation that simply maximizes p(vi |u) ○ May provide less obvious recommendations Incrementality p(vi |∅) p(vi |a) Incremental effect
- 41. ● Gold standard of causality ○ Random assignment ○ Measured across time ○ Incremental benefit of treatment ● Causality safety net? ○ Hard to test with full feedback loop effects ○ An algorithm may behave differently when training off its own data ○ Holdback tests A/B Testing Time A (Control) B (Treatment) Significant? Metrics
- 42. Conclusions.
- 43. ● After users and items, time is usually the next most important factor in recommendation systems ○ Model it as such ○ Evaluate it as such ○ Make it central to your system and infrastructure ● Recommender systems act in a causal loop ○ Influenced by themselves and others ○ Be thoughtful about feedback effects Takeaways
- 44. Thank you. @JustinBasilico @moustaki Justin Basilico & Yves Raimond Yes, we’re hiring...
- 45. Déjà Vu The Importance of Time and Causality in Recommender Systems Justin Basilico & Yves Raimond August 29, 2017 @JustinBasilico @moustaki