Beyond Whole Dialogue Modeling: Contextual Disentanglement for Conversational Recommendation (DisenCRS)

Paper: arXiv:2504.17427 Authors: Guojia An, Jie Zou, Jiwei Wei, Chaoning Zhang, Fuming Sun, Yang Yang Published: 2025-04-24

Background and Motivation

Conversational recommender systems (CRS) aim to provide personalized recommendations by analyzing and utilizing contextual information from dialogue. However, most existing methods model the dialogue context as a whole, ignoring the inherent complexity and entanglement within a conversation. In reality, a dialogue contains both:

• Focus information — directly tied to the user's actual needs and intent
• Background information — surrounding context that mutually interacts with the focus

Because current approaches model these two types of information in a mixed way, systems often misinterpret what users actually want, lowering recommendation accuracy.

The Proposed Model: DisenCRS

To address this, the paper proposes DisenCRS, a model that introduces contextual disentanglement into conversational recommendation. Its key components are:

1. Dual disentanglement framework — separates focus and background information from the dialogue context in a fully unsupervised setting:

• Self-supervised contrastive disentanglement
• Counterfactual inference disentanglement
2. Adaptive prompt learning module — automatically selects the most suitable prompt based on the specific dialogue context, fully exploiting the capabilities of large language models (LLMs).

Experimental Results

Experiments on two widely used public datasets show that DisenCRS significantly outperforms existing conversational recommendation models, achieving superior performance on both:

• Item recommendation
• Response generation

(Exact metric values should be verified against the original PDF tables.)

Insights for Search / Rec / Personalization

• Architecture: Retrieval + reranking + generation pipelines remain mainstream, but agentic paradigms increasingly treat retrieval count and strategy as learnable.
• Data: High-quality instruction data and click/session logs are equally critical; synthetic data needs safeguards against knowledge leakage and distribution shift.
• Evaluation: The gap between offline metrics and online satisfaction is widening; LLM-as-judge should be cross-validated with human evaluation.
• Deployment: Latency, cost, interpretability, and safety are hard constraints for industrial adoption — systems cannot be optimized for academic benchmarks alone.

Limitations and Future Directions

Potential limitations include experiment scale constrained by GPU budgets, mismatch between benchmarks and real user distributions, and unknown cross-lingual generalization. Future work may explore more efficient test-time compute allocation, deeper integration with knowledge graphs and structured databases, and causal/fairness constraints for recommendation.

Original Abstract

> Conversational recommender systems aim to provide personalized recommendations by analyzing and utilizing contextual information related to dialogue. However, existing methods typically model the dialogue context as a whole, neglecting the inherent complexity and entanglement within the dialogue. Specifically, a dialogue comprises both focus information and background information, which mutually influence each other. Current methods tend to model these two types of information mixedly, leading to misinterpretation of users' actual needs, thereby lowering the accuracy of recommendations. To address this issue, this paper proposes a novel model to introduce contextual disentanglement for improving conversational recommender systems, named DisenCRS. The proposed model DisenCRS employs a dual disentanglement framework, including self-supervised contrastive disentanglement and counterfactual inference disentanglement, to effectively distinguish focus information and background information from the dialogue context under unsupervised conditions. Moreover, we design an adaptive prompt learning module to automatically select the most suitable prompt based on the specific dialogue context, fully leveraging the power of large language models. Experimental results on two widely used public datasets demonstrate that DisenCRS significantly outperforms existing conversational recommendation models, achieving superior performance on both item recommendation and response generation tasks.

Related Reading

• A Survey on Multi-Turn Interaction Capabilities of Large Language Models (arXiv:2501.09959)
• Evaluating LLM-based Agents for Multi-Turn Conversations: A Survey (arXiv:2503.22458)
• Aligning Query Representation with Rewritten Query and Relevance Judgments (ACM TODIS, 10.1145/3627673.3679534)
• An Empirical Analysis on Multi-turn Conversational Recommender Systems (10.1145/3626772.3657893)
• CHIQ: Contextual History Enhancement for Improving Query Rewriting (arXiv:2406.05013)
• Few-Shot Conversational Dense Retrieval, SIGIR 2021 (10.1145/3404835.3462856)