mlsanityData to 7 Oct 2026

Probabilistic Rank and Reward: A Scalable Model for Slate Recommendation

arXiv 2208.06263 ↗First posted 10 Aug 2022cs.IR cs.LG stat.MLcited by 7

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Abstract (arXiv)

We introduce Probabilistic Rank and Reward (PRR), a scalable probabilistic model for personalized slate recommendation. Our approach allows off-policy estimation of the reward in the scenario where the user interacts with at most one item from a slate of K items. We show that the probability of a slate being successful can be learned efficiently by combining the reward, whether the user successfully interacted with the slate, and the rank, the item that was selected within the slate. PRR outperforms existing off-policy reward optimizing methods and is far more scalable to large action spaces. Moreover, PRR allows fast delivery of recommendations powered by maximum inner product search (MIPS), making it suitable in low latency domains such as computational advertising.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 17 of its 53 references that are in the map. First posted on arXiv 10 Aug 2022.

Builds on

17 of 53 references.

References · 17

  1. 2017Attention Is All You Needcited by 59,932
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2018Top-K Off-Policy Correction for a REINFORCE Recommender Systemcited by 181
  4. 2015Doubly Robust Policy Evaluation and Optimizationcited by 158
  5. 2014Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS)cited by 109
  6. 2016Off-policy evaluation for slate recommendationcited by 102
  7. 2018Deep Reinforcement Learning for Page-wise Recommendationscited by 99
  8. 2018Offline A/B testing for Recommender Systemscited by 94
  9. 2020Off-policy Bandits with Deficient Supportcited by 60
  10. 2020Counterfactual Evaluation of Slate Recommendations with Sequential Reward Interactionscited by 35
  11. 2021Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairnesscited by 35
  12. 2018Offline Evaluation of Ranking Policies with Click Modelscited by 32
5 more
  1. 2018Beyond Greedy Ranking: Slate Optimization via List-CVAEcited by 21
  2. 2020BLOB : A Probabilistic Model for Recommendation that Combines Organic and Bandit Signalscited by 19
  3. 2022Learning-to-Rank at the Speed of Sampling: Plackett-Luce Gradient Estimation With Minimal Computational Complexitycited by 13
  4. 2021Combining Reward and Rank Signals for Slate Recommendationcited by 4
  5. 2022Pessimistic Off-Policy Optimization for Learning to Rankcited by 2

Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 4; 2025: 1.

7 papers.

  1. 2024Logarithmic Smoothing for Pessimistic Off-Policy Evaluation, Selection and Learningcited by 16
  2. 2023Exponential Smoothing for Off-Policy Learningcited by 15
  3. 2024Bayesian Off-Policy Evaluation and Learning for Large Action Spacescited by 5
  4. 2023Fast Slate Policy Optimization: Going Beyond Plackett-Lucecited by 3
  5. 2024Unified PAC-Bayesian Study of Pessimism for Offline Policy Learning with Regularized Importance Samplingcited by 3
  6. 2024Position Paper: Why the Shooting in the Dark Method Dominates Recommender Systems Practice; A Call to Abandon Anti-Utopian Thinkingcited by 0
  7. 2025Off-Policy Learning in Large Action Spaces: Optimization Matters More Than Estimationcited by 0