# Causal Embeddings for Recommendation

arXiv: https://arxiv.org/abs/1706.07639 · HTML: https://mlsanity.com/paper/1706.07639/

## Key facts

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

## Built on by (most cited first, 30 of 79)

- [CoRAL: Collaborative Retrieval-Augmented Large Language Models Improve Long-tail Recommendation](https://mlsanity.com/paper/2403.06447/) (2024)
- [RAH! RecSys-Assistant-Human: A Human-Centered Recommendation Framework with LLM Agents](https://mlsanity.com/paper/2308.09904/) (2023)
- [Enhanced Doubly Robust Learning for Debiasing Post-click Conversion Rate Estimation](https://mlsanity.com/paper/2105.13623/) (2021)
- [Fairness in Recommendation: Foundations, Methods and Applications](https://mlsanity.com/paper/2205.13619/) (2022)
- [StableDR: Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Random](https://mlsanity.com/paper/2205.04701/) (2022)
- [Top-N Recommendation with Counterfactual User Preference Simulation](https://mlsanity.com/paper/2109.02444/) (2021)
- [How Do Recommendation Models Amplify Popularity Bias? An Analysis from the Spectral Perspective](https://mlsanity.com/paper/2404.12008/) (2024)
- [TDR-CL: Targeted Doubly Robust Collaborative Learning for Debiased Recommendations](https://mlsanity.com/paper/2203.10258/) (2022)
- [A Generalized Doubly Robust Learning Framework for Debiasing Post-Click Conversion Rate Prediction](https://mlsanity.com/paper/2211.06684/) (2022)
- [Balancing Unobserved Confounding with a Few Unbiased Ratings in Debiased Recommendations](https://mlsanity.com/paper/2304.09085/) (2023)
- [CausPref: Causal Preference Learning for Out-of-Distribution Recommendation](https://mlsanity.com/paper/2202.03984/) (2022)
- [Deconfounded Causal Collaborative Filtering](https://mlsanity.com/paper/2110.07122/) (2021)
- [Breaking Feedback Loops in Recommender Systems with Causal Inference](https://mlsanity.com/paper/2207.01616/) (2022)
- [Causal Collaborative Filtering](https://mlsanity.com/paper/2102.01868/) (2021)
- [Causal Disentanglement for Semantics-Aware Intent Learning in Recommendation](https://mlsanity.com/paper/2202.02576/) (2022)
- [Robust Collaborative Filtering to Popularity Distribution Shift](https://mlsanity.com/paper/2310.10696/) (2023)
- [Multiple Robust Learning for Recommendation](https://mlsanity.com/paper/2207.10796/) (2022)
- [Unbiased Knowledge Distillation for Recommendation](https://mlsanity.com/paper/2211.14729/) (2022)
- [Rec4Ad: A Free Lunch to Mitigate Sample Selection Bias for Ads CTR Prediction in Taobao](https://mlsanity.com/paper/2306.03527/) (2023)
- [Variation Control and Evaluation for Generative SlateRecommendations](https://mlsanity.com/paper/2102.13302/) (2021)
- [A Survey of Deep Causal Models and Their Industrial Applications](https://mlsanity.com/paper/2209.08860/) (2022)
- [Be Causal: De-biasing Social Network Confounding in Recommendation](https://mlsanity.com/paper/2105.07775/) (2021)
- [Local Policy Improvement for Recommender Systems](https://mlsanity.com/paper/2212.11431/) (2022)
- [Recommender systems based on graph embedding techniques: A comprehensive review](https://mlsanity.com/paper/2109.09587/) (2021)
- [Cross Pairwise Ranking for Unbiased Item Recommendation](https://mlsanity.com/paper/2204.12176/) (2022)
- [Debiased Recommendation with Noisy Feedback](https://mlsanity.com/paper/2406.17182/) (2024)
- [Doubly Calibrated Estimator for Recommendation on Data Missing Not At Random](https://mlsanity.com/paper/2403.00817/) (2024)
- [Unbiased Collaborative Filtering with Fair Sampling](https://mlsanity.com/paper/2502.13840/) (2025)
- [Correcting the User Feedback-Loop Bias for Recommendation Systems](https://mlsanity.com/paper/2109.06037/) (2021)
- [In the Eye of the Beholder: Robust Prediction with Causal User Modeling](https://mlsanity.com/paper/2206.00416/) (2022)

## 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.