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Time, Context and Causality in Recommender Systems
The document discusses the limitations of correlational recommender systems, emphasizing the need for causal models to improve recommendations based on temporal context. It outlines various approaches, including epsilon-greedy methods and instrumental variable models, each with their pros and cons in handling unobserved confounders and scaling issues. Ultimately, it concludes that effective recommendation algorithms must consider the immediate context and the causal nature of user interactions.
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Overview of the topic on time, context, and causality within recommender systems presented by Yves Raimond.
Discusses the need for quick content discovery and the importance of personalization for customer satisfaction.
Introduces the concept of correlational recommender systems using statistical models with features and outcomes.
Highlights the shortcomings of traditional models by emphasizing the importance of time and predictive accuracy.
Explains the need to incorporate time and context for better predictions in recommendation systems.
Defines causal recommender systems, emphasizing the need for causal modeling and its benefits over correlational approaches.
Outlines strategies like Epsilon-Greedy for selecting recommendations, discussing their advantages and disadvantages.
Presents approaches to tackle biases in recommendations, including the propensity model and instrumental variables.
Summarizes various recommendation strategies, their benefits, and drawbacks, highlighting the need for causal models.
Final thoughts on the necessity of understanding context, time, and causality in recommendation systems for effective outcomes.
Opens the floor for questions, inviting dialogue regarding the discussed recommender system strategies.
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Time, Context and Causality in Recommender Systems
- 1. Time, Context and Causalityin Recommender Systems Yves Raimond October 2018 France is AI @moustaki
- 2. ● A fewseconds to find something great to watch… ● Can only show a few titles ● Enjoyment directly impacts customer satisfaction ● How? Personalize everything, for 130M members across 190+ countries
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- 10. 0 1 01 0 0 0 1 1 0 1 0 0 1 1 0 1 0 0 0 0 0 0 0 1 UsersItems
- 11. 1 0 01 0 0 1 0 0 1 0 0 0 1 0 0 0 1 0 0 K K
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- 13. However... These models canserve you well, but ignore key aspects of the recommendation problem: Recommendations happen at a moment in time, in a given context
- 14. Time ● Don’t overfitthe past, focus on predicting the future ● Explicitly model temporal drift and system dynamics
- 15. Time + Context ●X: sequence of contextual user actions, plus current context ● Y: probability of next action ● E.g. “Given all the actions a user has taken so far, what’s the most likely video they’re going to play right now?”
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- 18. However... … correlational recommendersystems are missing something else: Recommendations are actions at a moment in time, in a given context
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- 22. Causal recommender systems ●p(Y|X) can be misleading ● Ignores the user in the loop ● Model the causal nature of a recommendation instead ● Two steps: ○ Model p(Y|X, do(R)) ○ Build policy, e.g. what R leads to max Y? (from http://www.tylervigen.com/spurious-correlations)
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- 25. Greedy Member Features Candidate Pool Model 1 Winner ProbabilityOf Engagement Model 2 Model 3 Model 4
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- 27. Approach 1 pros& cons ● Pros ○ Simple ○ Helps feedback loops & offline/online mismatches ● Cons ○ Exploration has a cost ○ Doesn’t scale well with size of the candidate pool
- 28. Approach 2: Propensities Policy p(R|X) + (X,R, Y) R Y Inverse propensity weighting X p(Y|X, R)
- 29. Approach 2 pros& cons ● Pros: ○ Model-agnostic ○ Simple ○ Scale well to large candidate sets ● Cons: ○ Only unbiased if no unobserved confounders ■ Hard to validate in practice ○ Variance can blow up ■ Variance regularization or weights clipping can help
- 30. Approach 3: InstrumentalVariable Example: What’s the causal impact of a recommendation? R YZ Noise OutcomeRec An instrumental variable Z can only influence the outcome Y through R Building causal models with instrumental variables: 2SLS (two-stage least-squares) or SGDIV (to be published)
- 31. Approach 3 pros& cons ● Pros: ○ Robust to unobserved confounders ● Cons: ○ Bias/Variance depends on strength of the IV ○ Harder to scale?
- 32. In summary... ● Epsilon-Greedy:simple, but expensive ● IPS: great and flexible, but doesn’t deal with unobserved confounders ● IV: deals with unobserved confounders, harder to scale ● Many more approaches available...
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- 34. Conclusions ● Recommendations areactions at a moment in time, in a given context ● Correlational algorithms are great, until they start driving most of the data ● Recommendation algorithms make decisions, which have consequences ● Causal models are required to reason about this impact
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