Dynamic Causal Collaborative Filtering

TL;DR

This paper introduces a dynamic causal graph with loops for recommender systems, analyzes conditions leading to echo chambers, and proposes a model that mitigates echo chambers while maintaining recommendation quality.

Contribution

It develops a causal graph with feedback loops for dynamic recommendation modeling and introduces a counterfactual reasoning approach to mitigate echo chambers.

Findings

The proposed framework effectively reduces echo chambers.

It maintains recommendation performance comparable to existing models.

Experimental results outperform state-of-the-art methods in echo chamber mitigation.

Abstract

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.