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 ($\partial$CCF) 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.

本文提出了一种具有循环过程的因果图并使用马尔可夫过程分析了回音室等数学特性，设计了一种动态因果协作过滤模型来减轻回音室现象并通过多次实验证明其优越性。