# Separating and Learning Latent Confounders to Enhancing User Preferences Modeling

arXiv: https://arxiv.org/abs/2311.03381 · HTML: https://mlsanity.com/paper/2311.03381/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 37 references that are in the map.
- First posted on arXiv 2 Nov 2023.

## Builds on (most cited first, 17 of 17)

- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [Recommendations as Treatments: Debiasing Learning and Evaluation](https://mlsanity.com/paper/1602.05352/) (2016)
- [Variational Inference of Disentangled Latent Concepts from Unlabeled Observations](https://mlsanity.com/paper/1711.00848/) (2017)
- [Identifying Causal Effects With Proxy Variables of an Unmeasured Confounder](https://mlsanity.com/paper/1609.08816/) (2016)
- [Causal Intervention for Leveraging Popularity Bias in Recommendation](https://mlsanity.com/paper/2105.06067/) (2021)
- [KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems](https://mlsanity.com/paper/2202.10842/) (2022)
- [Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System](https://mlsanity.com/paper/2010.15363/) (2020)
- [Causal Embeddings for Recommendation](https://mlsanity.com/paper/1706.07639/) (2017)
- [Modeling User Exposure in Recommendation](https://mlsanity.com/paper/1510.07025/) (2015)
- [Understanding Echo Chambers in E-commerce Recommender Systems](https://mlsanity.com/paper/2007.02474/) (2020)
- [User-controllable Recommendation Against Filter Bubbles](https://mlsanity.com/paper/2204.13844/) (2022)
- [A Model-Agnostic Causal Learning Framework for Recommendation using Search Data](https://mlsanity.com/paper/2202.04514/) (2022)
- [The Simpson's Paradox in the Offline Evaluation of Recommendation Systems](https://mlsanity.com/paper/2104.08912/) (2021)
- [Breaking Feedback Loops in Recommender Systems with Causal Inference](https://mlsanity.com/paper/2207.01616/) (2022)
- [Detect Professional Malicious User with Metric Learning in Recommender Systems](https://mlsanity.com/paper/2205.09673/) (2022)
- [A Unified Collaborative Representation Learning for Neural-Network based Recommender Systems](https://mlsanity.com/paper/2205.09670/) (2022)
- [REASONER: An Explainable Recommendation Dataset with Multi-aspect Real User Labeled Ground Truths Towards more Measurable Explainable Recommendation](https://mlsanity.com/paper/2303.00168/) (2023)

## Abstract (arXiv)

Recommender models aim to capture user preferences from historical feedback and then predict user-specific feedback on candidate items. However, the presence of various unmeasured confounders causes deviations between the user preferences in the historical feedback and the true preferences, resulting in models not meeting their expected performance. Existing debias models either (1) specific to solving one particular bias or (2) directly obtain auxiliary information from user historical feedback, which cannot identify whether the learned preferences are true user preferences or mixed with unmeasured confounders. Moreover, we find that the former recommender system is not only a successor to unmeasured confounders but also acts as an unmeasured confounder affecting user preference modeling, which has always been neglected in previous studies. To this end, we incorporate the effect of the former recommender system and treat it as a proxy for all unmeasured confounders. We propose a novel framework, Separating and Learning Latent Confounders For Recommendation (SLFR), which obtains the representation of unmeasured confounders to identify the counterfactual feedback by disentangling user preferences and unmeasured confounders, then guides the target model to capture the true preferences of users. Extensive experiments in five real-world datasets validate the advantages of our method.