# Dynamic Causal Collaborative Filtering

arXiv: https://arxiv.org/abs/2208.11094 · HTML: https://mlsanity.com/paper/2208.11094/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 63 references that are in the map.
- First posted on arXiv 23 Aug 2022.

## Builds on (most cited first, 30 of 30)

- [Session-based Recommendations with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06939/) (2015)
- [Recommendations as Treatments: Debiasing Learning and Evaluation](https://mlsanity.com/paper/1602.05352/) (2016)
- [Explainable Recommendation: A Survey and New Perspectives](https://mlsanity.com/paper/1804.11192/) (2018)
- [Deep Learning based Recommender System: A Survey and New Perspectives](https://mlsanity.com/paper/1707.07435/) (2017)
- [How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility](https://mlsanity.com/paper/1710.11214/) (2017)
- [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)
- [Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback](https://mlsanity.com/paper/1909.03601/) (2019)
- [Learning and Evaluating Graph Neural Network Explanations based on Counterfactual and Factual Reasoning](https://mlsanity.com/paper/2202.08816/) (2022)
- [Degenerate Feedback Loops in Recommender Systems](https://mlsanity.com/paper/1902.10730/) (2019)
- [Counterfactual Explainable Recommendation](https://mlsanity.com/paper/2108.10539/) (2021)
- [CauseRec: Counterfactual User Sequence Synthesis for Sequential Recommendation](https://mlsanity.com/paper/2109.05261/) (2021)
- [Understanding Echo Chambers in E-commerce Recommender Systems](https://mlsanity.com/paper/2007.02474/) (2020)
- [Personalized Counterfactual Fairness in Recommendation](https://mlsanity.com/paper/2105.09829/) (2021)
- [CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender System](https://mlsanity.com/paper/2204.01266/) (2022)
- [Neural Collaborative Reasoning](https://mlsanity.com/paper/2005.08129/) (2020)
- [PRINCE: Provider-side Interpretability with Counterfactual Explanations in Recommender Systems](https://mlsanity.com/paper/1911.08378/) (2019)
- [Political Discourse on Social Media: Echo Chambers, Gatekeepers, and the Price of Bipartisanship](https://mlsanity.com/paper/1801.01665/) (2018)
- [Counterfactual Explanations for Neural Recommenders](https://mlsanity.com/paper/2105.05008/) (2021)
- [Revisiting Alternative Experimental Settings for Evaluating Top-N Item Recommendation Algorithms](https://mlsanity.com/paper/2010.04484/) (2020)
- [Top-N Recommendation with Counterfactual User Preference Simulation](https://mlsanity.com/paper/2109.02444/) (2021)
- [Unbiased Learning for the Causal Effect of Recommendation](https://mlsanity.com/paper/2008.04563/) (2020)
- [Explainable Fairness in Recommendation](https://mlsanity.com/paper/2204.11159/) (2022)
- [Deconfounded Causal Collaborative Filtering](https://mlsanity.com/paper/2110.07122/) (2021)
- [Causal Collaborative Filtering](https://mlsanity.com/paper/2102.01868/) (2021)
- [Modelling Opinion Dynamics in the Age of Algorithmic Personalisation](https://mlsanity.com/paper/1811.03341/) (2018)
- [Maximizing the Diversity of Exposure in a Social Network](https://mlsanity.com/paper/1809.04393/) (2018)
- [Opinion Polarization by Learning from Social Feedback](https://mlsanity.com/paper/1704.02890/) (2017)
- [Tell me something my friends do not know: Diversity maximization in social networks](https://mlsanity.com/paper/1811.10354/) (2018)
- [Conformity, anticonformity and polarization of opinions: insights from a mathematical model of opinion dynamics](https://mlsanity.com/paper/1608.08810/) (2016)

## Built on by (most cited first, 2 of 2)

- [Advances in Automatically Rating the Trustworthiness of Text Processing Services](https://mlsanity.com/paper/2302.09079/) (2023)
- [Towards a Theoretical Understanding of Two Tower Recommendation Models](https://mlsanity.com/paper/2403.00802/) (2024)

## Abstract (arXiv)

Causal graph, as an effective and powerful tool for causal modeling, is usually assumed as a Directed Acyclic Graph (DAG). However, recommender systems usually involve feedback loops, defined as the cyclic process of recommending items, incorporating user feedback in model updates, and repeating the procedure. As a result, it is important to incorporate loops into the causal graphs to accurately model the dynamic and iterative data generation process for recommender systems. However, feedback loops are not always beneficial since over time they may encourage more and more narrowed content exposure, which if left unattended, may results in echo chambers. As a result, it is important to understand when the recommendations will lead to echo chambers and how to mitigate echo chambers without hurting the recommendation performance. In this paper, we design a causal graph with loops to describe the dynamic process of recommendation. We then take Markov process to analyze the mathematical properties of echo chamber such as the conditions that lead to echo chambers. Inspired by the theoretical analysis, we propose a Dynamic Causal Collaborative Filtering (partialCCF) model, which estimates users' post-intervention preference on items based on back-door adjustment and mitigates echo chamber with counterfactual reasoning. Multiple experiments are conducted on real-world datasets and results show that our framework can mitigate echo chambers better than other state-of-the-art frameworks while achieving comparable recommendation performance with the base recommendation models.