mlsanityData to 14 Oct 2026

Causal Embeddings for Recommendation

arXiv 1706.07639 ↗First posted 23 Jun 2017cs.IRcited by 79

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Abstract (arXiv)

Many current applications use recommendations in order to modify the natural user behavior, such as to increase the number of sales or the time spent on a website. This results in a gap between the final recommendation objective and the classical setup where recommendation candidates are evaluated by their coherence with past user behavior, by predicting either the missing entries in the user-item matrix, or the most likely next event. To bridge this gap, we optimize a recommendation policy for the task of increasing the desired outcome versus the organic user behavior. We show this is equivalent to learning to predict recommendation outcomes under a fully random recommendation policy. To this end, we propose a new domain adaptation algorithm that learns from logged data containing outcomes from a biased recommendation policy and predicts recommendation outcomes according to random exposure. We compare our method against state-of-the-art factorization methods, in addition to new approaches of causal recommendation and show significant improvements.

Key facts. Cited by 79 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 Jun 2017.

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Citing papers per year: 2021: 15; 2022: 21; 2023: 14; 2024: 12; 2025: 9; 2026: 8.

79 papers.

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  1. 2024CoRAL: Collaborative Retrieval-Augmented Large Language Models Improve Long-tail Recommendationcited by 44
  2. 2023RAH! RecSys-Assistant-Human: A Human-Centered Recommendation Framework with LLM Agentscited by 30
  3. 2021Enhanced Doubly Robust Learning for Debiasing Post-click Conversion Rate Estimationcited by 28
  4. 2022Fairness in Recommendation: Foundations, Methods and Applicationscited by 27
  5. 2022StableDR: Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Randomcited by 24
  6. 2021Top-N Recommendation with Counterfactual User Preference Simulationcited by 21
  7. 2024How Do Recommendation Models Amplify Popularity Bias? An Analysis from the Spectral Perspectivecited by 21
  8. 2022TDR-CL: Targeted Doubly Robust Collaborative Learning for Debiased Recommendationscited by 18
  9. 2022A Generalized Doubly Robust Learning Framework for Debiasing Post-Click Conversion Rate Predictioncited by 18
  10. 2023Balancing Unobserved Confounding with a Few Unbiased Ratings in Debiased Recommendationscited by 18
  11. 2022CausPref: Causal Preference Learning for Out-of-Distribution Recommendationcited by 17
  12. 2021Deconfounded Causal Collaborative Filteringcited by 14
38 more
  1. 2022Breaking Feedback Loops in Recommender Systems with Causal Inferencecited by 14
  2. 2021Causal Collaborative Filteringcited by 13
  3. 2022Causal Disentanglement for Semantics-Aware Intent Learning in Recommendationcited by 12
  4. 2023Robust Collaborative Filtering to Popularity Distribution Shiftcited by 12
  5. 2022Multiple Robust Learning for Recommendationcited by 11
  6. 2022Unbiased Knowledge Distillation for Recommendationcited by 11
  7. 2023Rec4Ad: A Free Lunch to Mitigate Sample Selection Bias for Ads CTR Prediction in Taobaocited by 11
  8. 2021Variation Control and Evaluation for Generative SlateRecommendationscited by 10
  9. 2022A Survey of Deep Causal Models and Their Industrial Applicationscited by 10
  10. 2021Be Causal: De-biasing Social Network Confounding in Recommendationcited by 9
  11. 2022Local Policy Improvement for Recommender Systemscited by 9
  12. 2021Recommender systems based on graph embedding techniques: A comprehensive reviewcited by 8
  13. 2022Cross Pairwise Ranking for Unbiased Item Recommendationcited by 8
  14. 2024Debiased Recommendation with Noisy Feedbackcited by 8
  15. 2024Doubly Calibrated Estimator for Recommendation on Data Missing Not At Randomcited by 7
  16. 2025Unbiased Collaborative Filtering with Fair Samplingcited by 6
  17. 2021Correcting the User Feedback-Loop Bias for Recommendation Systemscited by 5
  18. 2022In the Eye of the Beholder: Robust Prediction with Causal User Modelingcited by 5
  19. 2022RMT-Net: Reject-aware Multi-Task Network for Modeling Missing-not-at-random Data in Financial Credit Scoringcited by 5
  20. 2023Causal Lifting and Link Predictioncited by 5
  21. 2024Revisiting Reciprocal Recommender Systems: Metrics, Formulation, and Methodcited by 5
  22. 2021A framework for massive scale personalized promotioncited by 4
  23. 2021Estimating Causal Effects of Multi-Aspect Online Reviews with Multi-Modal Proxiescited by 4
  24. 2021Adversarial Learning for Incentive Optimization in Mobile Payment Marketingcited by 4
  25. 2022Causal Disentanglement with Network Information for Debiased Recommendationscited by 4
  26. 2022Bilateral Self-unbiased Learning from Biased Implicit Feedbackcited by 3
  27. 2023Pareto Invariant Representation Learning for Multimedia Recommendationcited by 3
  28. 2022Dynamic Causal Collaborative Filteringcited by 2
  29. 2022Causal Structure Learning with Recommendation Systemcited by 2
  30. 2023Uncertainty Calibration for Counterfactual Propensity Estimation in Recommendationcited by 2
  31. 2023Counterfactual Augmentation for Multimodal Learning Under Presentation Biascited by 2
  32. 2024Causal Disentanglement for Regulating Social Influence Bias in Social Recommendationcited by 2
  33. 2025RicciFlowRec: A Geometric Root Cause Recommender Using Ricci Curvature on Financial Graphscited by 2
  34. 2025Bi-Level Decision-Focused Causal Learning for Large-Scale Marketing Optimization: Bridging Observational and Experimental Datacited by 2
  35. 2026Causal Representation Learning for Generalisable Recommendationcited by 2
  36. 2021Online Evaluation Methods for the Causal Effect of Recommendationscited by 1
  37. 2021Learning Robust Recommender from Noisy Implicit Feedbackcited by 1
  38. 2021Causal Knowledge Guided Societal Event Forecastingcited by 1

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