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Counterfactual Language Reasoning for Explainable Recommendation Systems

Guanrong Li, Haolin Yang, Xinyu Liu, Zhen Wu, Xinyu Dai

TL;DR

This paper introduces a novel framework integrating structural causal models with large language models to establish causal consistency in recommendation pipelines, and enforces explanation factors as causal antecedents to recommendation predictions through causal graph construction and counterfactual adjustment.

Abstract

Explainable recommendation systems leverage transparent reasoning to foster user trust and improve decision-making processes. Current approaches typically decouple recommendation generation from explanation creation, violating causal precedence principles where explanatory factors should logically precede outcomes. This paper introduces a novel framework integrating structural causal models with large language models to establish causal consistency in recommendation pipelines. Our methodology enforces explanation factors as causal antecedents to recommendation predictions through causal graph construction and counterfactual adjustment. We particularly address the confounding effect of item popularity that distorts personalization signals in explanations, developing a debiasing mechanism that disentangles genuine user preferences from conformity bias. Through comprehensive experiments across multiple recommendation scenarios, we demonstrate that CausalX achieves superior performance in recommendation accuracy, explanation plausibility, and bias mitigation compared to baselines.

Counterfactual Language Reasoning for Explainable Recommendation Systems

TL;DR

This paper introduces a novel framework integrating structural causal models with large language models to establish causal consistency in recommendation pipelines, and enforces explanation factors as causal antecedents to recommendation predictions through causal graph construction and counterfactual adjustment.

Abstract

Explainable recommendation systems leverage transparent reasoning to foster user trust and improve decision-making processes. Current approaches typically decouple recommendation generation from explanation creation, violating causal precedence principles where explanatory factors should logically precede outcomes. This paper introduces a novel framework integrating structural causal models with large language models to establish causal consistency in recommendation pipelines. Our methodology enforces explanation factors as causal antecedents to recommendation predictions through causal graph construction and counterfactual adjustment. We particularly address the confounding effect of item popularity that distorts personalization signals in explanations, developing a debiasing mechanism that disentangles genuine user preferences from conformity bias. Through comprehensive experiments across multiple recommendation scenarios, we demonstrate that CausalX achieves superior performance in recommendation accuracy, explanation plausibility, and bias mitigation compared to baselines.

Paper Structure

This paper contains 28 sections, 18 equations, 4 figures, 8 tables.

Table of Contents

  1. Introduction
  2. Method
  3. Task Formalization
  4. Causal View of Explainable Recommendation
  5. Model Structure
  6. User-Item Matching
  7. Explanation Selection
  8. Candidate Explanations Generation
  9. Causal Analyse of Popularity Bias
  10. Debias Explanation Selection
  11. LLM-Based Recommendation
  12. Experiments
  13. Experimental settings
  14. Datasets
  15. Baselines
  16. ...and 13 more sections

Figures (4)

  • Figure 1: The proposed causal graph: (a) illustrates the fundamental phenomena that reflect real-world scenarios, (b) illustrates the post-hoc methods, (c) represents the parallel methods, and (d) depicts the enhanced version of real-world scenarios, including item popularity.
  • Figure 2: Overall Structure of CausalX. LLMs Generate Candidate Explanations, Followed by the Debias Explanation Selection Module, and Final Recommendations Based on Selected Explanations
  • Figure 3: The proposed causal graph models counterfactual scenarios. (a) represents the counterfactual world to estimate the total effect, and (b) illustrates the counterfactual world to estimate the effect of $P \rightarrow E$. Here, $U^*, I^*, M^*, P^*$ denote the counterfactual values of the user, item, user-item matching, and popularity, respectively.
  • Figure 4: Trajectory of the explanations and recommendation performance.